International Journal of Medical Engineering and Informatics (112 papers in press)

Regular Issues

• Adaptive Neuro-Fuzzy Inference System for the diagnosis of non-mechanical low back pain  
  by Mehrdad Farzandipour, Ehsan Nabovati, Esmaeil Fakharian, Hossein Akbari, Soheila Saeedi 
  Abstract: Back pain is one of the most important causes of disability. Clinical Decision Support Systems (CDSSs) can help physicians diagnose diseases with greater precision. This study designs and implements a CDSS to diagnose non-mechanical Low Back Pain (LBP), including spinal brucellosis, ankylosing spondylitis, spinal tuberculosis, and spinal osteoarthritis using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The highest corrected classification percentage was related to Spinal brucellosis (82.8%), and CDSS was able to differentiate four non-mechanical LBP types.
  Keywords: Clinical Decision Support System; Non-Mechanical Low Back Pain; Adaptive Neuro-Fuzzy Inference System; Diagnose.
  
  
• ICG signal noise cancellation algorithms for non-invasive hemodynamic monitoring  
  by Hadjer Benabdallah, Salim Kerai 
  Abstract: Impedance cardiography (ICG) non-invasive technique is a new way in the medical field, used for diagnosis and monitoring of cardiovascular disorders. The signal processing domain developed several denoising techniques applied for the cancellation of respiratory and movements' artefacts that corrupted the acquired signal shape. For this main purpose, our paper-based on a comparative study between different type of adaptive filters and Savitzky-Golay (SG) filtering, those are applied to the sensed impedance to noise removal for hemodynamic parameters monitoring. The results demonstrated in this work are more suitable; it confirmed that the proposed SG denoising filter outperformed the other techniques cited in state-of-art in terms of preserving information carried over on the signal when the mean error rate of the SG technique is equal to 0.00038%.
  Keywords: impedance cardiography; ICG; adaptive filters; Savitzky-Golay; mean error rate.
  DOI: 10.1504/IJMEI.2022.10047842
   
  
• Improving prediction of one-year mortality of acute myocardial infarction using machine learning techniques  
  by Mirza Touseef, Najla Raza, Adeel Zafar, Muhammad Zubair, Saad Zafar 
  Abstract: The purpose of our study was to improve the prediction of one-year mortality for patients with acute myocardial infarction (AMI). We implemented and compared four classical machine learning algorithms and one deep neural network algorithm. For evaluation metrics, we used accuracy, F1-measure, precision, recall, and area under the receiver operating characteristic curve (AUC). Random forest achieved the best performance based on an AUC of 0.98 with an accuracy of 92%. Results show that our model can predict one-year mortality of AMI with an improved AUC and accuracy using a minimum number of features as compared to previous related studies.
  Keywords: machine learning; deep neural networks; acute myocardial infarction; AMI; mortality prediction; cardiovascular diseases; CVDs.
  DOI: 10.1504/IJMEI.2021.10041072
   
  
• Estimation techniques of vocal fold disorder: a survey  
  by Satyajit Pangaonkar, Reena Gunjan 
  Abstract: Voice disorder of the speech has been observed as an utmost critical issue in both children and elders. Complexity, limited experimentation and predictions by the physicians during the analysis are main causes for inaccurate diagnosis and wrong information about the dysfunction. The research studies are limited to elementary analysis and diagnosis level for the physicians and therapists due the restrictions of skilled specialists and the expenses involved. This paper deals with the evaluation and facts to understand software tools, different acoustic, non-acoustic and non-invasive approach. This can be observed in terms of sensors and image extraction analysis to deal with the pathologies and to overcome the problems which will be beneficial to the speech language pathologists, clinicians, therapist and vocalist.
  Keywords: FonaDyn; PRAAT; multidimensional voice program; MDVP; tuned electromagnetic resonator collar sensor; TERC; polyvinylidene fluoride; PVDF; physiological microphone sensing; physiological microphone sensor; P-MIC; high-speed video; HSV; VKG.
  DOI: 10.1504/IJMEI.2023.10052324
   
  
• A new Parkinson detection system based on evolutionary fast learning networks and voice measurements  
  by Ayoub Bouslah, Nora Taleb 
  Abstract: Parkinsons disease (PD) is become the second most neurological syndrome of the central nervous system after Alzheimers disease. It causes diverse symptoms which include bradykinesia (slowness of movement), voice impairments, rigidity, tremor, and poor balance. The PD recognition system based on voice is founded a noninvasive alternative, but involves rather complex measurements or variables. Therefore an attention is required toward new approaches for better forecasting accuracy. In this paper, an optimal Fast Learning Network (FLN) based on genetic algorithm (GA) was established as PD Diagnosis system. FLN is a double parallel feed-forward neural network structure, as a matter of fact, GA for features reduction and hyperparameters optimization of the FLN which is used as a predictive model. Finally, the conducted experiments on the Parkinson data of voice recordings over Ten Fold Cross-Validation show that proposed system is less complex and also achieved better average classification results with an accuracy of 97.47 %. At the same time, its effective in automatic identification of important vocal features. Moreover, the highest average degree of improved accuracy was (2.1 %) compared with other familiar wrappers including Support Vector Machine and K-nearest Neigbors in the similar conditions.
  Keywords: Parkinson’s disease (PD); Fast Learning Network(FLN); genetic algorithm (GA); features reduction; hyperparameters optimization; predictive model; PD Diagnosis system.
  
  
• Classification of Electroencephalography Signals using Three-Dimensions Convolution Neural Network with Long Short Term Memory Architecture  
  by Viet Quoc Huynh, Hoang-Thuy-Tien Vo, Thu Anh Nguyen, Tuan Van Huynh 
  Abstract: This research attempts to use the electroencephalography signals based on physiological signals extracted from the database for emotion analysis to classify the emotion of the subjects by classifier neural network algorithms. In this work, two types of neural network including 3D convolution neural network and hybrid network (3D convolution neural network model combined with long short term memory architecture) were applied to train and test its ability of emotion states classification. As a result, the hybrid network gave the most efficient classification with an accuracy of around 80\%, which was better than other algorithms such as Support Vector Machine, Random Forest, Convolution Neural Network. Furthermore, the results also showed that the accuracy achieved differently at various frequency bands, in which Delta frequency band gave the highest accuracy. Combining signals of different frequencies helped to improve the classification efficiency.
  Keywords: Electroencephalography; Convolution neural network; LSTM; Hybrid network.
  
  
• Current status of Hydroxychloroquine and Azithromycin for the Treatment of COVID-19: An Observational Review  
  by PRATEEK PRATYASHA, ADITYA PRASAD PADHY 
  Abstract: Though effect coronavirus has known to be a catastrophic pandemic since 100 of years, Severe Acute Respiratory Syndrome-2 coronavirus (SARS2-CoV) was first claimed to be emerged in December 2019 at the city of Wuhan, China. Abruptly, the virus dominated more than 218 countries with 157,566,607 confirmed cases and the death figure has reached nearly 32,84,551 till time. Recently the pandemic is getting worse day by day, people are suffering from hypoxia and server respiratory problem despite of the unstoppable service of healthcare sector. Prior concern behind this emergency is that, till date researchers and scientists were failed to invent any productive pharmaceutical treatment to weed out the infection completely. Although, vaccination is publically available; but it is applicable only for precaution purpose and not evident for preventive measures. This review focuses on therauptiuc status to control the severity of SAS2-CoV agent. The approach aims at implicating a low toxic metabolite anti-malarial drug, Hydroxychloroquine combined with an antibiotic called Azithromycin for the treatment of acute respiratory disturbance and hypoxia. This article briefly demonstrates the phramaco-potential of both these medications, their effects on patients based on a clinical observation and ongoing status of dosage to validate its implication.
  Keywords: Severe Acute Respiratory Syndrome2-CoV (SARS2-CoV); COVID-19; Hydroxychloroquine; Azithromycin; Choloroquine.
  
  
• Comparison between whole exome sequencing and the FoundationOne  
  by Catherine Wellan, Hillary Rayo, Shashi Mehta 
  Abstract: This systematic review and meta-analysis examines the question of how effective the FoundationOne
  Keywords: tumor mutation burden; TMB; whole exome sequencing; WES; FoundationOne; targeted panels; next-generation sequencing; solid tumors; immune checkpoint blockade therapies; ICB; correlation; method comparison studies.
  
  
• Integration of Intelligence in software development process for implementing a secure healthcare system - a review  
  by N. Asha, Siva Rama Krishnan, J. Gitanjali 
  Abstract: The implication of artificial intelligence (AI) has made substantial evolution in the modern environment. It has innovatively transformed the technical world and has assimilated everything in the real life. The impact of AI in the software development and process has significantly improved the progression with its cognitive behaviour. In modern-days, there is a remarkable increase in the data management; computational vitality has risen at reduced cost. Lot more breakthroughs are happening in technology, AI is performing all these seamlessly. To present the overall role of AI in software development and process, we have analysed the state-of-the-art of AI in software development for privacy in healthcare. The work is exemplified by recent advances in product development process and AI for securing medical data. Further, we also investigate on various phases of software development process where AI can be embedded for developing an efficient and secured healthcare system especially in the analysis of electronic medical records (EMR). We also discuss the challenges in incorporating AI in healthcare application.
  Keywords: artificial intelligence; AI; security; privacy; healthcare; fault detection; electronic medical records; EMR.
  DOI: 10.1504/IJMEI.2021.10041120
   
  
• Identification of stroke using deepnet machine learning algorithm  
  by Abdulwahhab Alshammari, Noorah Atiyah, Hanoof Alaboodi, Riyad Alshammari 
  Abstract: Stroke is a disease that impacts individuals of all races, genders, and backgrounds. To combat the rising prevalence of the disease, the creation of accurate diagnostic tools is paramount. This paper uses two machine learning models, deepnet and decision tree, to assess the health record data from the Ministry of National Guard Health Affairs, Saudi Arabia. Deepnet outperformed the decision tree in accurately predicting stroke and stroke mimic. Deepnet achieved an accuracy of 92.35%, while decision tree achieved 90.8%. Future application of machine learning on stroke and stroke mimic diagnosis holds great potential in public health reform, patient empowerment, and minimising healthcare burden. This paper proposes building a national centralised semi-detection stroke data management framework to create a national platform in the diagnoses, acute, and long-term treatment of stroke.
  Keywords: stroke; machine learning; identification.
  DOI: 10.1504/IJMEI.2021.10041722
   
  
• Hepatitis C virus and cirrhosis: an analysis of incidence and cost of management of disease in the USA  
  by Pratima Tandon, Dinesh Mital, Shankar Srinivasan, Riddhi Vyas 
  Abstract: This study utilised the national (nationwide) impatient sample (NIS), which is part of the healthcare cost and utilisation project (HCUP). A study was conducted to determine the incidence and cost of management of Hepatitis C (HC) and cirrhosis (C) patients. The incidence rate of HC is 34% and C is 30%. HC is higher in the age group of 21-65-year old's whereas C prevalent in people that are 66-80+ years. The HC count is higher for Medicaid and, C is for Medicare payment methods. HC patients have a high Emergency admission rate. C patients show higher rate to transfer to a short-term hospital, home healthcare, length of stay in the hospital, death rate during hospitalisation, frequency of biopsy procedures, charges for Liver Transplant, charges for admission to the trauma Centre as well as emergency admission. 62% of males have HC and 61% have C. For females 37% have HC and 39% C. HC and C is higher in Urban locations and low-income group. In average liver transplant is higher for the age group of 21 to 80 years. Asian people are less predisposed to getting HC.
  Keywords: hepatitis; hepatitis c; cirrhosis; SPSS; data analysis; HCUP; NIS; liver disease; cost; charges; length of stay; health outcomes; incidence of hepatitis.
  DOI: 10.1504/IJMEI.2021.10041773
   
  
• Clinical decision support system for the diagnosis of Hepatitis C virus  
  by Pratima Tandon, Dinesh P. Mital, Shankar Srinivasan, Riddhi Vyas 
  Abstract: A new clinical decision support system (CDSS) was developed using Exsys Corvid for the diagnosis of Hepatitis C virus. This CDSS is medically accurate and can guide healthcare professionals through the diagnostic process. Corvid Exsys rule-based system is used for building automated expert systems. The software utilises backward and forward chaining techniques. All the questions asked by the system during the diagnosis process are based on the clinical literature. The system can guide a clinician through the diagnostic process to achieve hepatitis results. Decision-making expert system was successfully developed to diagnose the Hepatitis C virus.
  Keywords: hepatitis; Hepatitis C; clinical decision support system; CDSS; decision support systems; jaundice.
  DOI: 10.1504/IJMEI.2021.10041821
   
  
• Developing a biotechnical scheme using fuzzy logic model for classification of severity of pyelonephritis  
  by Nikolay Korenevskiy, Seregin Stanislav Petrovich, Riad Taha Al-Kasasbeh, Ayman Ahmad Alqaralleh, Sofia Nikolaevna Rodionova, Ashraf Adel Shaqadan, Ilyash Maxim Yurievich, Mahdi Salman Alshamasin 
  Abstract: The aim of the work is to develop fuzzy logic model to process health data involving oxidative indicators in patients with pyelonephritis to predict the severity level of pyelonephritis as severe and purulent forms. A 13 immunity and oxidative health indicators (lipid peroxidation) are used for classification of disease severity. A control sample of patient's is analysed to develop class's and experienced physicians are consulted to modify considered class limits. The fuzzy logic model gives high accuracy in diagnosis of serous and purulent pyelonephritis in patients with urolithiasis. Verification of the results of the operation of the obtained decision rules on the control sample showed that the proposed method's diagnostic efficiency reaches 93%, which is acceptable for use in medical practice.
  Keywords: pyelonephritis; serious and purulent form; fuzzy logic; mathematical models; differential diagnosis; oxidative status.
  DOI: 10.1504/IJMEI.2021.10041822
   
  
• Identification and classification of schizophrenic speech using convolutional neural network for medical healthcare  
  by Akshita Abrol, Nisha Kapoor, Parveen Kumar Lehana 
  Abstract: Schizophrenia is a brain disorder that significantly affects the quality of life of affected individuals. One of its prominent symptoms is the induction of changes in the acoustics of the patients. In the absence of definite methods for its diagnosis, speech analysis can help in the preliminary screening of the patients. In this paper, an automated method using deep learning for differentiating between individuals with schizophrenia and psychosis from healthy individuals is suggested. Using convolutional neural networks with speech spectrograms as input, a classification accuracy of 87.01% has been obtained for levels of schizophrenia and 95.26% for differentiating between schizophrenic and healthy speech.
  Keywords: schizophrenia; convolutional neural network; CNN; deep learning; spectrograms.
  DOI: 10.1504/IJMEI.2021.10041905
   
  
• Automatic speech classification in school children with cleft (and lip) palate  
  by Khaled Baazi, Mhania Guerti 
  Abstract: The automatic detection of emphatic occlusive [t] in pathological speech (PS) in schoolchildren with cleft lip and palate can provide diagnostic information to clinicians and speech therapists. We propose an automatic classification system for PS by the decision tree method (DT) to use it in rehabilitation in Algerian hospitals. Acoustic analysis was applied in order to extract the relevant acoustic characteristics of this type of PS. The results showed that the DT adapts well to the classification of the PS with a significant rate ratio (%) of the PS, for the case of the long vowels 87% and 85% for short vowels.
  Keywords: cleft palates; pathological speech; decision tree; classification; school children.
  DOI: 10.1504/IJMEI.2021.10041983
   
  
• CIAE: class imbalance aware ensemble framework to predict drug side effects  
  by Kanica Sachdev, Manoj Kumar Gupta 
  Abstract: The binding of the drug compounds to certain biological off target proteins causes undesirable side effects or drug toxicology. The determination of drug toxicology at the early steps of drug development would help to economise on money as well as time. The paper proposes a novel framework, class imbalance aware ensemble (CIAE), for the identification of drug side effects using ensemble learning. It employs the related side effect information of the drugs to predict novel side effects. An eminent cause of the low performance of the machine learning based methods is the presence of class imbalance in the data. The proposed framework efficiently addressees this issue to improve the predictor performance. A comprehensive comparison of the method with the state of the art classifiers shows that the proposed framework yields better results for drug side effect determination.
  Keywords: biological targets; class imbalance; drugs; drug side effects; drug toxicology; ensemble classifier; machine learning.
  DOI: 10.1504/IJMEI.2021.10041984
   
  
• An overview of early detection of Alzheimer's disease  
  by C.R. Nagarathna, Kusuma Mohanchandra 
  Abstract: Alzheimer's disease is a most common neurodegenerative brain disease characterized by a problem in thinking and loss in cognition memory judgment. One of the difficult tasks is to identify symptoms of Alzheimer's at its beginning stage. Once the disease is detected it is very serious to save the life of a patient. The current medical treatments cannot cure Alzheimer's but its early diagnosis can stop the progress of disease and make the patient lead their life. So it is necessary to diagnose Alzheimer's at an early stage using different modalities like EEG, MRI, and PET as they provide more promising results compared to a single modality In this paper, the progression symptoms, causes, modalities, and methods used for finding Alzheimer's disease is reviewed. The state-of-the- art literature is reviewed and the gaps identified in the literature are discussed.
  Keywords: Alzheimer's; early detection; cognitive; modalities.
  DOI: 10.1504/IJMEI.2021.10042418
   
  
• Comparing the performance of machine learning techniques for low back pain diagnosis  
  by Hamid Bouraghi, Sorayya Rezayi, Soheila Saeedi, Rasoul Salimi, Meysam Jahani, Sajjad Abdolmaleki 
  Abstract: Low back pain is a global health problem that is a major cause of disability in developing and developed countries. Machine learning and data mining algorithms can be used to help diagnose this disease. This study aimed to determine the performance of different machine learning algorithms. Nine machine learning techniques, including support vector machine, decision tree, Naive Bayes, K-nearest neighbours, neural network, random forest, deep learning, auto-MLP, and rule induction, were used to modelling. This study revealed that the highest accuracy was related to the random forest (83.55%) and support vector machine (82.26%) classifiers. As a result, machine learning algorithms have good accuracy in low back pain diagnosis.
  Keywords: machine learning; data mining; low back pain; LBP; diagnose.
  DOI: 10.1504/IJMEI.2021.10043054
   
  
• Efficient breast cancer detection using novel intensity partitioning-based clustering algorithm and multi-dimensional LSTM cyclic neural network  
  by Gul Shaira Banu Jahangeer, T.Dhiliphan Rajkumar 
  Abstract: Recently, early detection of breast cancer is significant to reduce the mortality rate, especially in women. Hence, the study aims to classify breast cancer from digital database for screening mammography (DDSM) dataset using partition and intensity based segmentation algorithm and modified convolutional neural network-long short-term memory (CNN-LSTM) classifier. Initially, pre-processing is performed using Gaussian filtering by taking the mammogram image. Then, it is segmented using a novel intensity partitioning-based clustering algorithm (IPCA). Further, feature extraction is performed and finally, classification is implemented using a novel multi-dimensional LSTM cyclic neural network (MLSTM-CNN). The analysis is performed to evaluate the efficiency of the proposed system and the outcomes explored its efficacy in breast cancer detection.
  Keywords: intensity partitioning; segmentation; long short-term memory; LSTM; cyclic neural network; CNN; breast cancer classification; digital database for screening mammography; DDSM; intensity partitioning-based clustering algorithm; IPCA.
  DOI: 10.1504/IJMEI.2021.10043507
   
  
• Multi-task deep neural network models for learning COVID-19 disease representations from multimodal data  
  by Veena Mayya, K. Karthik, Krishnananda Prabhu Karadka, S. Sowmya Kamath 
  Abstract: Over the continued course of the COVID-19 pandemic, a significant volume of expert-written diagnosis reports has been accumulated that capture a multitude of symptoms and observations on diagnosed COVID-19 cases, along with expert-validated chest X-ray scans. The utility of rich, latent information embedded in such unstructured expert-written diagnosis reports and its importance as a source of valuable disease-specific information has been explored to a very limited extent. In this work, a convolutional attention-based dense (CAD) neural model for COVID-19 prediction is proposed. The model is trained on the rich disease-specific parameters extracted from chest X-ray images and expert-written diagnostic text reports to support an evidence-based diagnosis. Scalability is ensured by incorporating content based learning models for automatically generating diagnosis reports of identified COVID-19 cases, reducing radiologists’ cognitive burden. Experimental evaluation showed that multimodal patient data plays a vital role in diagnosing early-stage cases, thus helping hasten the diagnosis process.
  Keywords: COVID-19 diagnosis; clinical decision support systems; multi-task learning; healthcare analytics; deep convolution neural networks.
  DOI: 10.1504/IJMEI.2021.10043617
   
  
• Computerised detection of autism spectrum disorder using EEG signals  
  by Aarti Sharma 
  Abstract: Autism spectrum disorder (ASD) is one of the most common neurological disorders. Detection of ASD is based on behavioural analysis made by clinician by conducting interviews with the parents of the child. This paper presents computer aided diagnostic tool to detect autism disorder. This paper presents the detection of ASD based on biological markers. An early diagnosis is essential to confirm that the child have ASD. In this paper, power spectral density and phase locked values have been extracted from gamma band of EEG signals for autistic and normal subjects. Gamma band from EEG signals is extracted using band pass filter. Significant decrement in these features is observed for the autistic subjects in comparison to normal subjects. Above findings are statistically validated though ANOVA. Anomalies in EEG signals can be used as potential biomarker for detection of ASD.
  Keywords: autism; electroencephalogram; EEG; gamma wave; computer aided diagnosis.
  DOI: 10.1504/IJMEI.2021.10043643
   
  
• Biothermodynamics may be a highly useful tool to help medical practitioners to detect and cure morbidities  
  by Mustafa Özilgen 
  Abstract: Biothermodynamics may help clinicians to diagnose and cure many medical cases, slow down ageing, correct malnutrition and repair the distorted tissues. Thermodynamically detected decrease in cardiac or renal filtration efficiencies indicate a malfunctioning system. Thermodynamic assessment of the circulatory system after amputation and restoring the circulatory pressure drop may prevent deaths. Research on thermodynamics of fertilisation may help to medical practitioners to fight against infertility. Athletes of some sports have substantially shorter lifespan than the other people. Biothermodynamics may offers some help to those athletes via prescribing appropriate energy intake. Distortion of the energy management in the body is observed in various health problems, including cancer. The need for multidisciplinary research to achieve additional understanding of these phenomena has been suggested by numerous researchers. Biothermodynamics may offer the best tool to achieve this goal.
  Keywords: biothermodynamics; internal work performance; external work performance; energy and exergy efficiency; helping medical practitioners; diagnosing and curing morbidities.
  DOI: 10.1504/IJMEI.2021.10043802
   
  
• Risk stratification of cardiovascular disease in type 2 diabetes using LDA and CNN for clinical decision management - a multi-centre study in eastern India  
  by Suparna Dutta, Saswati Mukherjee, Medha Nag, Sujoy Majumdar, Ghanshyam Goyal 
  Abstract: Approximately 72.9 million patients of type 2 diabetes mellitus (T2DM) in India are at a potential risk of cardiovascular diseases (CVDs), strokes and peripheral gangrene. CVD is a major cause of disability and death is one of the major areas of risk severity stratification study. Unlike well-known prediction score models of CVD herein, a unique assessment deep learning model is proposed to stratify the cardiovascular events in different risk grades in T2DM individuals This risk assessment tool can aid clinicians in decision management of CVD risk severity It is a retrospective cross-sectional observational study that stratifies risks using linear discriminant analysis (LDA) and convolution neural network (CNN). Class separability feature of LDA helps to achieve optimal performance. The model is externally validated in a cohort of 4,719 individuals with T2DM to assess performance heterogeneity across different settings.
  Keywords: convolution neural network; CNN; linear discriminant analysis; LDA; support vector machine; SVM; risk stratification; cardiovascular disease; CVD; type 2 diabetes mellitus; T2DM; India.
  DOI: 10.1504/IJMEI.2021.10043803
   
  
• Global sensitivity analysis implications on the design of PF-04475270 model predictive control system  
  by Omer Hamid 
  Abstract: In this study, we adopted Pharmacokinetic-Pharmacodynamic (PKPD) of Pfizer ophthalmic drug 5-{3-[(2S)-2-{(3R)-3-hydroxy-4-[3-(trifluoromethyl) phenyl] butyl}-5-oxopyrrolidin-1-yl] propyl} thiophene-2-carboxylate (PF-04475270) from literature and performed a series of global sensitivity analysis (GSA) to identify the most influential PKPD parameters of PF-04475270. We used the sensitivity analysis for everybody (SAFE) toolbox GSA with two different well-established GSA methods. Our GSA analyses successfully identified the aqueous chamber transfer rates as high-ranking parameters in the comparison with all the other 11 different model parameters. Our data with prioritised parameters suggest the need for probing the aqueous drug concentration for future design of model predictive control (MPC) release system of PF-04475270.
  Keywords: global sensitivity analysis; GSA; model parameters uncertainties; predictive control; Iris-Ciliary body; pharmacokinetics; pharmacodynamics; sensitivity analysis for everybody; SAFE; model predictive control; MPC.
  DOI: 10.1504/IJMEI.2021.10043868
   
  
• Cloud and online system of 3D printing for serving multi-client of hospitals and medical colleges in different locations  
  by Ignatius Luddy Indra Purnama, Alva Edy Tontowi, Herianto 
  Abstract: This manuscript presents a 3D printing method, especially human bone anatomy, in a cloud and online system. The human bone anatomy, specific skull and foot bone, of the patient’s digital imaging and communications in medicine (DICOM) file, is uploaded to the so-called 3DPNet-DICOM cloud. After printing, the 3DPNet-DICOM cloud sends a notification and the 3D printed bone model to the hospital by courier. The 3D images constructed from the DICOM file and the 3D printed model was identifiable with no significant dimensional errors. The result is to eliminate the human operators’ activity, and queuing at the order process.
  Keywords: 3D printing; bone model; cloud application; online application; DICOM; network access.
  DOI: 10.1504/IJMEI.2021.10043869
   
  
• A conceptual model to improve the patient flow during COVID-19  
  by Abdesselam Bougdira, Asmae Mazti, Hayat Sedrati, Fayssal Jhilal, Saaid Amzazi, Chakib Nejjari, Hassan Ghazal 
  Abstract: In the COVID-19 context, hospitals across the globe endeavour to manage the unprecedented flow of patients. Challenges are related to the unexpected increase in patients, extra waiting time for cleaning and decontamination of inpatient beds, congested queueing in treatment areas, and waiting time in crowding rooms. This research introduces a new conceptual model to bring a broader, patient-centred, and traceability-based view of patient flow in a clinical setting in the context of COVID-19. The model design opts for four interrelated modes procedural, technical, operational, and contextual. These modes lay the basis of a patient flow solution before starting its development. The feasibility of the proposed model is shown through a usage scenario and developed user interfaces. Results have shown that a knowledge representation of the patient’s real-time information can enable better patient flow monitoring. In addition, it would support intelligent healthcare systems that reuse and share pieces of declarative knowledge. Such functionality would enhance the management of patient flow and improve dealing with the patient flow challenges imposed by the COVID-19 pandemic. Moreover, the presented proof of concept laid the ground for future applicability in various clinical settings facing similar infectious disease crises.
  Keywords: patient flow; clinical setting; COVID-19; traceability; ontology; artificial intelligence; conceptual modelling; hospital systems.
  DOI: 10.1504/IJMEI.2022.10044040
   
  
• Detecting obstructive sleep apnea by extracting multimodal HRV features using ensemble subspace discriminant classifier  
  by Nivedita Singh, R.H. Talwekar 
  Abstract: Obstructive sleep apnea disorder is very peculiar sleep disorder which is triggered because of rapid and repeated transition of breathing. Hypopnea is also known as partial blockage of respiration during sleep. Polysomnography is gold standard to detect OSA but it is very expensive and complex which motivates us to detect OSA through multimodal heart rate variability (HRV) feature analysis using single channel ECG. The comparison among three classifiers SVM, weighted KNN and ensemble subspace discriminant are investigated for OSA detection. The accuracy obtained by the ESD classifier is 100%. True positive rate (TPR) and the true negative rate (TNR) have been attained 100% which is best suitable classifier for our experiment.
  Keywords: obstructive sleep apnea; ensemble subspace discriminant; heart rate variability; multimodal features.
  DOI: 10.1504/IJMEI.2022.10044042
   
  
• Analysis on effect of resampling techniques on cardiac arrhythmia classification using convolutional neural networks  
  by Rekha Rajagopal, V. Shyam Kumar 
  Abstract: Cardiac arrhythmia is a condition in which the heart beats at a faster rate or a slower rate instead of a regular rhythm. Medical professionals identify the category of cardiac arrhythmia by manually viewing the electrocardiogram (ECG) signal which is more time-consuming. There are possibilities of incorrectly identifying the arrhythmia categories due to practical difficulties in manually assessing the slight variations in the amplitude of ECG signals. This research work focuses on automatically categorising the heartbeats as normal beat, supraventricular beat, ventricular beat, fusion beat, and unknown beat using convolutional neural network. The class imbalance problem that arises because of a few numbers of heartbeats in certain arrhythmia categories is resolved using techniques such as synthetic minority oversampling technique (SMOTE), borderline SMOTE, SVM-SMOTE, and adaptive synthetic sampling (ADASYN). The proposed model demonstrates an average accuracy of 97.76 % in classifying arrhythmia classes using ADASYN technique. This model can help medical professionals in accurately diagnosing the arrhythmia classes.
  Keywords: cardiovascular disease; electrocardiogram; ECG; arrhythmia; convolutional neural network; CNN; class imbalance.
  DOI: 10.1504/IJMEI.2022.10044232
   
  
• Machine learning approach to detect congenital heart diseases using palmar dermatoglyphics  
  by Y. Mahesha, C. Nagaraju 
  Abstract: The present article has proposed a machine learning method to detect congenital heart diseases (CHDs) such as atrial septal defect (ASD) and myocardial infarction (MI) based on the frequency of occurrence of palm patterns such as ulnar loop and whorl. The system has been developed based on SSD-MobileNet to detect ulnar loop and whorl patterns on palm image. The developed system has achieved an accuracy of 99.28% and 97.19% in the detection of ulnar loop and whorl respectively. Further, the work has been carried out to fix the threshold value on the number of ulnar loop and whorl patterns to detect CHDs such as ASD and MI. The receiver operating characteristic curve has been drawn and the area under curve is calculated for the detection of ASD and MI. These results have shown that the proposed method can be used as a screening model to detect ASD and MI.
  Keywords: ulnar loop; whorl; atrial septal defect; ASD; myocardial infarction; SSD-MobileNet.
  DOI: 10.1504/IJMEI.2022.10044266
   
  
• Spotting congenital heart diseases using palm print based on faster R-CNN and spatial method  
  by Y. Mahesha, C. Nagaraju 
  Abstract: This paper proposes a machine learning method to detect congenital heart diseases (CHDs) using a palm pattern known as axial triradius. This article spreads light on three things. First, Faster R-CNN Inception v2 has been used to identify triradii on the palm image. Secondly, a novel spatial method has been proposed to select leftmost, rightmost and axial triradii. Finally, the angle at axial triradius has been calculated on the palm images of healthy people and of patients suffering from tetralogy of Fallot (TOF), atrial septal defect (ASD), ventricular septal defect (VSD) and coarctation of aorta (CoA). The result shows that the proposed method can be used as a screening method to predict CHDs.
  Keywords: axial triradius; CHDs; faster R-CNN; inception v2; spatial method.
  DOI: 10.1504/IJMEI.2022.10044267
   
  
• Feasibility of automatic differential diagnosis of endodontic origin periapical lesions - a pilot study  
  by Jay Patel, Dinesh Mital, Vaishali Singhal, Shankar Srinivasan, Huanmei Wu, Sashi Mehta 
  Abstract: Periapical dental diseases (PDD) are one of the most prevalent dental diseases leading to tooth loss and poor quality of life. Differential diagnosis of periapical diseases is critical because the treatment plan depends on the diagnosis. The current diagnosis approach of PDD uses an eyeballing method mainly depending on clinicians’ expertise, any lack of which may lead to an inaccurate diagnosis. We developed an advanced image processing tool that can help clinicians for more accurately differential PDD diagnoses that leads to the correct treatment approach. Sixty periapical radiographs were processed by the tool and the differential diagnostic output was compared with manually annotated gold standard dataset. Our tool performed well with a 95% sensitivity, 89% specificity, and 93% accuracy in providing a differential diagnosis. Demonstrating such promising results, further studies should test the accuracy of this tool on a larger dataset get more definitive results for widespread deployment and use.
  Keywords: image processing; dentistry; periapical radiographs; automatic differential diagnosis; dental informatics; image processing; dental image processing; digital dentistry.
  DOI: 10.1504/IJMEI.2022.10044462
   
  
• Real-time healthcare monitoring system through haversine distance calculation-based global positioning system  
  by Leo John Baptist, Linesh Raja, Suresh Shanmugasundaram 
  Abstract: In the present world, new technologies are highly-developed which drastically changes the conventional tasks of medical and the healthcare systems. Evolving mobile health (m-health) systems belong to these technologies with advanced data communication, artificial intelligence, deep learning, big data, cloud computing, and other machine learning approaches. Data collected using sensors are sent to the local databases via cellular networks which are the stored in the cloud. Data residing in the cloud or medical centres are used for analysis. Machine learning approaches are utilised for predicting the disease appropriately and for classification purpose. This research paper details the m-health systems, their framework with GPS assistance using machine learning. Moreover, this model can save the human life for some time as the ambulance location is tracked using GPS. Heart beat rate and temperature of patient are sensed and the information is sent to the hospital or to the mobile of the doctor.
  Keywords: global positioning system; GPS; healthcare; longitude; latitude; m-health.
  DOI: 10.1504/IJMEI.2022.10044463
   
  
• Analysis of chest x-ray images using deep learning approaches  
  by Ruchika Arora, Indu Saini, Neetu Sood 
  Abstract: Common thorax diseases such as pneumonia, tuberculosis, are diagnosed with digital radiography, i.e., chest X-ray (CXR) images. This paper provides a glimpse of chest abnormalities classification and annotation methods for CXR images that improves work efficiency and diagnosis accuracy. At present, pre-trained models such as ResNet, DenseNet, and its variants have become important deep learning (DL) approaches for successful classification and detection of diseases. This detailed literature review highlights need for integration of both image and text metadata features for designing multi-label image classification systems for effective diagnosis of chest diseases. As novel coronavirus disease (COVID-19) causes lung problems so a new research frontier is to fight against COVID-19. This paper covers an insight into literature review of DL algorithms used for diagnosis of COVID-19, and also emphasises move from computer aided detection (CAD) to the clinic illustrating recent practices, problems, and up-to-date information on CXR image classification and annotation.
  Keywords: chest X-ray images; deep learning; machine learning; ResNet; DenseNet; image classification; disease detection; image captioning; COVID-19.
  DOI: 10.1504/IJMEI.2022.10044515
   
  
• Empirical assessment of COVID-19 infections and information diffusion: a data science approach  
  by Isaac Kofi Nti, Adebayo Felix Adekoya, Owusu Nyarko-Boateng, Ponnadurai Ramasami 
  Abstract: The spread of the novel coronavirus disease, SARS-CoV-19 (COVID-19), has affected human activities everywhere, resulting in fear and panic among all age groups. Hence, this study implements a novel data science process to empirically model the daily reported cases and Google search queries in 14 countries. We observed a strong positive association (0.79-0.96) among reported cases of COVID-19 in the 14 countries. Furthermore, there is an inverse correlation of -0.18 to -0.62 between information diffusion on the virus and reported cases (new cases and deaths). Our outcome shows that contagious diseases are highly predictable using historical records from other countries and information spread on the disease.
  Keywords: data analytics; machine learning; data science; coronavirus; SARS-CoV-2; COVID-19; Goggle search engine; infections.
  DOI: 10.1504/IJMEI.2022.10044968
   
  
• How health knowledge that can influence patient outcome  
  by Leandro Pereira, Carlos H. Jeronimo, André Salgado, Álvaro Dias, Renato Lopes Da Costa, Rui Gonçalves 
  Abstract: Since COVID-19 was declared a global pandemic, it has killed more than two million people worldwide. Some directly due to complications and symptoms of the virus others due to lack of resources to take care of all the patients affected by it. A proper triage can go a long way to an efficient resource allocation, which makes it extremely relevant to understand which factors can affect COVID-19 complications or mortality risk. With the analysis of survey data collect form front-line Portuguese doctors, it was possible to identify the main comorbidities and health issues that can influence patient outcome.
  Keywords: COVID-19; mortality risk; comorbidities; severity of symptoms; knowledge management.
  DOI: 10.1504/IJMEI.2022.10045013
   
  
• Colorectal cancer risk factor assessment in Palestine using machine learning models  
  by Mohammad A. Z. Abu Zuhri, Mohammed Awad, Shahnaz Najjar, Nuha El Sharif, Issa Ghrouz 
  Abstract: Colorectal cancer (CRC) has a prevalence of 15% among men and 14.6% among women of all cancers. This research was carried out to assess behavioural risk factors that affected Palestinian reported CRC cases, and to make use of machine learning (ML) tools that might be used in CRC prediction, where the use of a public CRC classification and prediction tool based on accurate ML tools might help individuals in addressing their behavioural CRC risk factors and enhancing their engagement with their health. In this research, Palestinian dataset that consists of 57 predictors was collected, the dataset consists of 216 instances. Statistical models were used to determine the important features. The study found that the most important risk factors to consider are age, past medical history, diet behaviours, physical activity, and obesity. Consequently, ML models were applied to classify and predict CRC risk factors. Results showed that ANNs outperformed all models.
  Keywords: colorectal cancer; CRC; data mining; risk factors; machine learning; classification; Palestine.
  DOI: 10.1504/IJMEI.2022.10045260
   
  
• Computational study of the progression of Alzheimer's disease and changes in hippocampal theta rhythm activities due to beta-amyloid altered calcium dependent ionic channels  
  by Akanksha Kaushik, Jyotsna Singh, Shilpa Mahajan 
  Abstract: Although, Amyloid beta (B-amyloid) and neurofibrillary tangles are the assay mark of Alzheimer’s disease (AD), cognitive decline is best concerned with synaptic loss, rather than tangles or plaques. The pyramidal neurons in Hippocampus are highly affected by AD. Therefore, pyramidal neurons are prime focus in our study. Pyramidal neurons have extensively developed calcium signalling, a phenomenon that controls the neuronal rhythms desirable for memory processing and cognition, for regulating wide range of functions like controlling rhythmic activities, information processing and memory binding. The focus of our work is to inspect the impact of B-amyloid on calcium signaling and progression of AD through computational study.
  Keywords: Alzheimer’s disease; calcium signalling; pyramidal neurons; hippocampus.
  DOI: 10.1504/IJMEI.2022.10045261
   
  
• A deep neural network-based architecture for automated detection of COVID-19 from chest X-ray images  
  by Abul Abbas Barbhuiya, Ram Kumar Karsh, Rahul Jain, Hillol Phukan 
  Abstract: The COVID-19 pandemic has a severe adverse impact on people’s health, lives, and the worldwide global economy. It is only possible to identify positive COVID-19 instances if each country carries out sufficient tests. As suggested in prior studies, X-Ray images could be used as testing samples to develop a reliable and low-cost COVID-19 testing model. This paper introduces a deep learning-based end-to-end binary classification framework, Att-Net, for automated detection of COVID-19 cases using Chest X-ray images. In this work, we have adopted pre-trained ConvNet (VGG-16) with an attention module embedded with the VGG-16 architecture, which significantly improves the model’s performance. The proposed architecture is evaluated on the COVID-Xray-5k dataset. The suggested methodology obtains a state-of-the-art sensitivity of 98.5% and specificity of 99.4%. This work also presents a detailed performance analysis in terms of accuracy, sensitivity, specificity, precision, recall, and F-score. Furthermore, we have also generated the heat maps, which reveal the most anticipated regions infected by COVID-19 while learning for prediction by the CNN to validate the proposed architecture.
  Keywords: COVID-19; pandemic; deep learning; ConvNet; machine learning; transfer learning; feature extraction; X-ray images; healthcare management; CNN.
  DOI: 10.1504/IJMEI.2022.10045268
   
  
• Identifying key gait parameters in gender recognition and classification performance analysis using machine learning algorithms  
  by Neha Sathe, Anil Hiwale, Archana Ranade 
  Abstract: Different gait parameters retrieved through pressure sensors, classification spatial, statistical, temporal and demographic (SSTD) model is suggested and tested for gender recognition and classification. Combination of spatial, temporal and demographic features along with performed descriptive statistics is use to train the model. Support vector machine, logistic regression and k-nearest neighbour classification results are tested and analysed for precision and recall. Step length and stride length with weight and height provides great performance in achieving accuracy. Classification results within range of 80% to 90% for selected dataset of healthy 80 subjects were achieved. Influence of stride length in female and step length in male recognition along with single support time is observed. Contribution of weight is also recognisable in classification accuracy. Behaviour of female recognition and classification provides clear results on selected features using SSTD model while precision and recall values whereas male recognition values are on lower end.
  Keywords: spatiotemporal parameters; support vector machine; SVM; k-nearest neighbour; KNN; logistic regression; LR; gender recognition.
  DOI: 10.1504/IJMEI.2022.10046133
   
  
• An actigraph data-based early diagnosis of depression using ensemble classifiers  
  by C.D. Anisha, N. Arulanand, R. Rekha 
  Abstract: Depression is one of the severe mental disorders which prevails as one of the key symptoms in unipolar and bipolar disorder. An early diagnosis of depression can lead to quicker recovery. This paper proposes an artificial intelligence (AI)-based early diagnosis system for depression using the actigraph motor data. The key contribution of the paper is the 'ensemble classifiers' which is a type of machine learning (ML) model, a subpart of AI model, which improves the diagnosis of depression state by combining the predictions of various single classifiers. The result signifies that the proposed system with ensemble classifier AI model has an accuracy of 85% which is reliable and consistent than existing systems.
  Keywords: bipolar disorder; depression; actigraph; ensemble classifiers.
  DOI: 10.1504/IJMEI.2022.10045834
   
  
• Tele-health consultations during COVID times - barriers and facilitators: a multi-state exploratory study from India  
  by Sree T. Sucharita, Chakrapani Chatla, Vikhram Ramasubramanian, R. Vaishika, S.M. Balaji, R. Pradeep, Aravind Manoharan 
  Abstract: Globally, COVID-19 has drastically derailed the clinical care for outpatient services. Healthcare professionals (HCPs) used various specially designed tele-health applications (THA) to cater to the needs of patients. We attempted to explore the barriers and facilitators in utilising THAs as part of a multi-state and multi-disciplinary exploratory sequential mixed methods study. Our study comprised of initial survey of HCPs using quantitative Google Forms Survey tool followed by personal (physical and phone-based) interviews of 30 selected/consented HCPs. Of the 98 HCPs responded to the survey, ~60% of had 1-2 telehealth consultations/day during the pandemic. Approximately 61% of the consultations were limited to < 60 minutes interactions. Less than half were obtaining consent and only one third were maintaining digital log record. Most of the younger HCPs especially women have continued their services through THAs and felt that THAs will continue in the future. Besides the limitations of generalisability, the results suggest enormous scope for THAs in future towards simplifying the processes for effective healthcare service delivery at lesser cost and time.
  Keywords: COVID-19; tele-health applications; THA; healthcare service delivery; digital platforms; India.
  DOI: 10.1504/IJMEI.2022.10045849
   
  
• A new segmentation method for retinal pathologies detection in optical coherence tomography images  
  by Ben Khelfallah Amel, Messadi Mahammed, Lazouni Mohammed El Amine 
  Abstract: Diabetic macular oedema (DME) and age-related macular degeneration (AMD) are the leading causes of blindness in adults. The most significant signs of these diseases are appearance of exudates and change of retinal layer structure. Screening of these diseases is very important to prevent vision loss. In this work, a new method based on a genetic k-means algorithm for lesions detection is proposed. From the selected region of interest (ROI), four textural features are extracted and used to classify these two retinal diseases against the normal subjects using the SD-OCT images. From the experimental results found, the SVM gives better results for AMD and DME recognition. The mean accuracy, sensitivity and specificity values for the macular regions classification are 99.67%, 100% and 99.51% respectively.
  Keywords: OCT images; age-related macular degeneration; AMD; diabetic macular oedema; DME; features extraction; classification; genetic algorithms.
  DOI: 10.1504/IJMEI.2022.10046139
   
  
• Automated classification of cervical cells using integrated VGG-16 CNN model  
  by Rajesh Yakkundimath, Varsha S. Jadhav, Basavaraj S. Anami 
  Abstract: The most popular method for early cervical cancer screening and detection is the Pap-smear. Automatic analysis of Pap-smear images using computer technology will help in the accurate classification of cervical cancer cells. In this paper, a deep learning approach based on VGG-16 convolutional neural network (CNN) model integrated with support vector machine (SVM) classifier is proposed to identify and classify the cervical cells. A deep convolutional generative adversial network (DCGAN) framework is employed to generate the required synthetic Pap-smear images. The best average classification result of 96.24% is achieved on the held-out dataset comprising 16,124 images belonging to five classes of cervical cells.
  Keywords: cervical cancer; Pap-smear images; data augmentation; classification.
  DOI: 10.1504/IJMEI.2022.10046152
   
  
• Feature importance analysis for a highly unbalanced multiple myeloma data classification  
  by Rima Guilal, Nesma Settouti, Gonzalo Martínez-Munoz, Mohammed Amine Chikh 
  Abstract: Multiple myeloma (MM) is a hematological cancer associated with abnormal plasma cell proliferation. Its diagnostic process is long because it is very difficult to discover it at an early stage. This paper presents an approach to aid in MM diagnosis and staging. Tree-based ensemble learning methods are used to measure the features importance in models constructed for predicting MM stages. Comparative analysis showed that random forest outperformed other algorithms with an accuracy of over 97%; however, XGBoost gives a ranking of features considered most prognostic for MM staging. A discussion of results with specialists in hematology supported and validated our proposed study.
  Keywords: blood cancers; multiple myeloma; prognostic factors; ensemble learning; feature importance; unbalanced data; grid search.
  DOI: 10.1504/IJMEI.2022.10046878
   
  
• Effectiveness of machine learning for mental health: observing the mental state of Bangladeshi people  
  by Sayda Umma Hamida, Narayan Ranjan Chakraborty 
  Abstract: Analysing and finding the most used AI applications in the mental health sector and advising appropriate directions for advanced research is the intention of this research. With this purpose, authors commenced a systematic review by analysing selected 31 articles and found several neuroimaging and recognising technologies in real life for checking brain abnormalities. Besides, it revealed from the study that bot is the most used AI assistant in digital care. However, the authors surveyed the young people (aged between 19-29) of Bangladesh to identify mental disorders like as- anxiety, depression, and PTSD. The authors used Python to analyse the dataset, find correlations, and applied machine learning classification algorithms (e.g., decision tree, support vector machine, random forest) to measure the accuracy. The researchers explained a few threats of mental instability in their findings and offered several directions for future research using virtual and real-life AI technologies.
  Keywords: AI; mental-health; anxiety; depression; PTSD; Chatbot.
  DOI: 10.1504/IJMEI.2022.10046879
   
  
• New bio-inspired approach for deep learning techniques applied to neonatal seizures  
  by Mohamed Akram Khelili, Sihem Slatnia, Okba Kazar, Seyedali Mirjalili, Samir Bourekkache, Guadalupe Ortiz, Yizhang Jiang 
  Abstract: Neonatal seizures are a common emergency in the neonatal intensive care unit and their detection using electroencephalography (EEG) recording is one of the biggest challenges that neurologists face. Even though using artificial intelligence methods such as deep learning for computer vision can help to solve these problems, time consumption, complexity, and overfitting or underfitting of the model still limit the application of deep learning. In order to produce a real-time system that can detect neonatal seizures using EEG and solve the problem of the lack of availability of neurologists, a convolution neural network-based marine predator algorithm system is proposed.
  Keywords: neonatal seizures; electroencephalography; EEG; artificial intelligence; deep learning; convolution neural network; CNN; parallel metaheuristic optimisation; marine predators algorithm; MPA; genetic algorithm.
  DOI: 10.1504/IJMEI.2022.10046880
   
  
• Neoteric machine learning approaches to diagnose the state of carotid artery  
  by Hariharan Anantharaman, Navendu Chaudhary, Vimal Raj 
  Abstract: Develop machine learning models based on the information, data elements and images captured on a carotid ultrasound. Initiated with capture, collation and compilation of comprehensive carotid ultrasound reports of patients. Next analyse, extract, clean and compile data for the development of model. In this neoteric approach, a set of supervised algorithms and image-deep learning algorithms are implemented, different models built and tested. Performance of all the models is par excellence with a majority delivering accuracy over 75%. All the models, based on the varied machine learning algorithms, delivered acceptable and consistent accuracy - few models have even surpassed and delivered superior accuracy.
  Keywords: classifier; supervised learning; carotid ultrasound; stenosis; machine learning; deep learning.
  DOI: 10.1504/IJMEI.2022.10046881
   
  
• Cloud-based electronic health record sharing and access controlling blockchain architecture using data de-identification method  
  by Munshi Rejwan Ala Muid, Afrin Jubaida, Md. Mehedi Hasan Onik, Hamim Hamid 
  Abstract: Electronic health record (EHRs) demands the highest privacy and security as the accuracy of medical research and service largely depend on the integrity of it. Intruders can tamper sensitive protected health information (PHI) or personally identifiable information (PII) purposefully. This study aims to use blockchain technology to build a complete EHR sharing and tracking system, such that even after access is granted to researchers or medical personnel, EHRs remain unaltered and the identities of patients are hidden. This work uses a traditional cloud storage system to store information while saving the indexes of EHRs in a chain to ensure PHI integrity. Additionally, the data sharing is secured by applying a randomly selected data de-identification method that guarantees integrity and trackability of user data anonymously. Finally, this work demonstrates a complete architectural implementation (using Hyperledger and a private blockchain network) of the proposed EHR sharing and access controlling by using two levels of chain (two distinct ledgers) that ensures an efficient sharing platform of PHI.
  Keywords: electronic health records; EHR; blockchain; private cloud; data de-identification; security; access control.
  DOI: 10.1504/IJMEI.2022.10046882
   
  
• An energy efficient reconfigurable architecture for multi-lead ECG signal compression  
  by Vinod Arunachalam, N. Kumareshan 
  Abstract: The most used non-invasive diagnostic technique for a wide range of cardiac disorders is an ECG, which records the heart’s electrical activity over time. Compressed ECG signals are a necessary part of most electronic health systems to store and transmit data across long distances. The field programmable gate array (FPGA), a high-speed parallel compute unit, and customisable software capabilities are available with reconfigurable architecture. Consequently, this architecture is suitable for devices like ECGs, which require precise real-time computing for multi-channel signal processing. The Xilinx Zynq 7.000 SoC development board used in this work has an FPGA-based reconfigurable signal processing unit. When compressing data, the method uses fast fourier transformation (FFT). It is possible to achieve a 90% compression rate with this system running in real-time and with minimal to no signal distortion. This method is also the only one in the industry that can reduce high-frequencynoise.
  Keywords: ECG; reconfigurable architecture; health system.
  DOI: 10.1504/IJMEI.2022.10047060
   
  
• Biotechnical neural network system for predicting cardiovascular health state using processing of bio-signals  
  by Sergei Filist, Riad Taha Al-Kasasbeh, Olga Vladimirovna Shatalova, Mohammad Hjouj Btoush, Manafaddin Namazov, Ashraf Adel Shaqadan, Mahdi Alshamasin, Nikolay Korenevskiy, Saleh Aloqeili, Maxim Borisovich Myasnyankin 
  Abstract: In this study, for the early diagnosis of cardiovascular diseases, a multimodal classifier is built, in which three groups of heterogeneous data are used. The data is classified by autonomous intelligent agents with subsequent aggregation of their solutions at the next hierarchical level of classification. As one of the lower-level classifiers, a classifier is used, built on descriptors obtained on the basis of monitoring and analysing the evolution of the amplitudes of the harmonics of the 0.1 Hz systemic rhythm. The presented architecture of the multimodal classifier showed an increase in the accuracy of the diagnostic efficiency by 11%.
  Keywords: system rhythms; signal demodulation; electrocardiosignal; spectral analysis; neural networks.
  DOI: 10.1504/IJMEI.2022.10047451
   
  
• A review on applications of near infrared spectroscopy technique for neonatal monitors  
  by Pooja Gohel, Vijay Dave 
  Abstract: In current scenario, to avoid infant mortality especially preterm infants with unknown cause is a big challenge for neonatal hospital personnel. In spite of observation with currently available monitoring devices, there has been remarkable infant death happening at the hospital site. So, need for a device arises that monitor major vital parameters reflecting assessment of important organs such as brain and heart on a continuous basis. Near infrared spectroscopy (NIRS) is the recent technique that measures vitals such as regional cerebral oxygenation (rScO2), tissue perfusion, and related demand-supply of blood at specific organ in continuous, non-invasive, real-time quantitative manner. Many articles have been published showing wide applications of NIRS technique to compare the recorded rScO2 signal under various diseases conditions. This paper gives review of NIRS based measurement of vitals for infants, its applications and effectiveness.
  Keywords: near infrared spectroscopy; NIRS; preterm infant; tissue perfusion; neonatal monitors; regional cerebral oxygenation.
  DOI: 10.1504/IJMEI.2022.10047493
   
  
• Automated system-based classification of lung cancer using machine learning  
  by Vidhi Bishnoi, Nidhi Goel, Akash Tayal 
  Abstract: Lung malignant growth is the well-known reason for death identified due to cancer worldwide. Therefore, to help the radiologist to detect it correctly, automated computer techniques come up with several machine learning classification. For such an automated technique, machine learning algorithms have been applied for the classification of CT scan lung images. This includes two proposed novel features Gabor energy, Gabor entropy, and five grey level co-occurrence matrix (GLCM). These new features are distinct and help in boosting the performance of the classifier to achieve higher accuracy. The proposed method has been simulated on 450 CT scan lung images acquired from the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) dataset. As a result, The accuracy of 100%, 99%, 83%, and 92% have been achieved from support vector machine (SVM), neural networks (NN), Naive Bayes (NB), and perceptron, respectively.
  Keywords: image processing; lung cancer; CT images; Lung Image Database Consortium; LIDC; machine learning; Gabor filter; Gabor energy; Gabor entropy.
  DOI: 10.1504/IJMEI.2022.10047638
   
  
• Chest x-ray image analysis for pneumonitis disease classification  
  by Ruchika Arora, Indu Saini, Neetu Sood 
  Abstract: Computer-aided health system increase doctors diagnosing capability and drastically reduces patients’ death. This paper introduces an algorithm with combinational approach of convolution neural network (CNN) and gated recurrent unit (GRU) for pneumonia detection on low-cost chest X-ray (CXR) images. This model practices potential of multiple GRUs with CNN and fuses spatial and label information of CXR images for pixel-level classification. The proposed CNN+GRU model is experimented on pneumonia CXR image dataset available at Kaggle, which consists of 5,216, 624 train and test images respectively. The proposed model achieves 99.74% and 98.37% accuracy on training and testing dataset respectively.
  Keywords: artificial intelligence; disease detection; image classification; convolution neural network; chest images; gated recurrent units; GRU; pneumonia disease; Kaggle; lung diseases; medical image diagnosis.
  DOI: 10.1504/IJMEI.2022.10047778
   
  
• FoodKnight: a mobile educational game and analyses for obesity awareness of children  
  by Thomas Baily, Fadi Thabtah, Marcus Wright, Duy Anh Tran 
  Abstract: One of the main contributing factors towards child obesity is deficient education regarding food choices. In most cases, children will choose energy-dense foods over those with more nutritional value, without understanding the consequences of their decisions. To help overcome this problem, this paper proposes and assesses the efficacy of an educational gaming application called FoodKnight. Games have the ability to engage children more than traditional teaching schooling methods. FoodKnight incorporates stealth learning to disguise the teaching of healthy food choices while playing a game, while simultaneously incorporating a step-counter to encourage the user to be active. Survey results received from 38 participants regarding the initial prototype have been positive, with minor issues being addressed in an upcoming update. FoodKnight has been shown to be an effective tool for subconsciously conditioning children to make healthier choices.
  Keywords: educational gaming application; food choices; medical informatics; mobile development; stealth learning; software development.
  DOI: 10.1504/IJMEI.2022.10048016
   
  
• A novel method to detect and classify Alzheimer using naive Bayes classification algorithm  
  by C. Ravichandran, T. Senthil Kumar, K. Balaji, Kavitha Sandanam, Chandrasekaran Sivakumaran 
  Abstract: Digital medical imaging technology has been more accessible to the general people in recent years. This study describes a software strategy for detecting brain irregularities to diagnose Alzheimer’s disease. The suggested method creates a 3D model of the brain using MRI slices. This method is more precise and reliable. In this research, we proposed two strategies. To extract radiological data from MRI images, image processing and machine learning were utilised, and a deep learning approach was applied to analyse the condition of Alzheimer’s disease. The algorithm normalises and removes the skull from the MRI images in the first phase. Using a modified K-Means approach, the image is split into white matter (WM), grey matter (GM), and black holes (BH). The classifier is trained using the training data to predict the test data. The characteristics are defined using naive Bayes to create a classification model.
  Keywords: Alzheimer’s disease; magnetisation resonance image; MRI; mild cognitive impairment; magnetic resonance imaging; deep learning; residual neural network.
  DOI: 10.1504/IJMEI.2022.10048130
   
  
• Machine learning based wearable sensor module for human fall detection - a fully functional solution  
  by Juluru Anudeep, Shriram K. Vasudevan, T.S. Murugesh 
  Abstract: This paper addresses a potential concern faced by the majority of aged ones left unaccompanied at home. Any untoward fall owing to poor health conditions or slippery floors that are more prevalent among the elders happen, it usually goes unnoticed and the aged ones are deprived of the potentially life saving 'golden hour' treatment. To mitigate such sort of problems faced by the elderly, we have designed a wrist-wearable fall detection system that employs a machine learning model for movement tracking and to detect a fall just in case. During a fall, an automated call is generated to the emergency services as well as to a caretaker through a GSM module. Two datasets are collected, trained and tested on seven different machine learning models, and the results presented.
  Keywords: machine learning; fall detection; wearable device; medical applications.
  DOI: 10.1504/IJMEI.2022.10048147
   
  
• Healthcare services: applications, trend, and challenges  
  by Abderrazak Sebaa, Nabil Djebari, Abdelkamel Tari 
  Abstract: Recently, a large amount of heterogeneous health-related data and services are generated daily. Therefore, managing these medical flows of data and services requires complex and costly techniques. Moreover, the big service paradigm has received growing attention in various disciplines. This study aims to review and investigate the impact of service computing on health and medical sector. It illustrates the big service challenges, applications and describes how healthcare will benefit from service computing advances. A number of potential research opportunities related to the big service computing paradigm and underlying issues that require longer-term work are also discussed in this paper.
  Keywords: big service; big data; web service; medical services.
  DOI: 10.1504/IJMEI.2022.10048151
   
  
• Brain image compression and reconstruction system using deep learning  
  by S. Seenuvasamurthi, S. Ashok, B. Shankarlal, A. Mohamed Abbas, Ashok Vajravelu 
  Abstract: New perspectives on brain structure and function can only be gained through the rapid advancement of brain imaging technology. Throughout history, this has been the case. It is common practise in medicine to employ image processing in the early stages of diagnosis and treatment. In classification and segmentation tasks, deep neural networks (DNNs) have so far proven to be exceptional. Functional ultrasound (fUS) is a novel imaging technique that enables the observation of neuronal activity across the brain in awake, ambulatory rats. To achieve adequate blood flow sensitivity in the brain microvasculature, fUS relies on lengthy ultrasonic data collecting at high frame rates, placing a load on the sampling and processing hardware. Parallel MRI is introduced in broad terms, with an emphasis on the classical understanding of image space and k-space-based techniques.
  Keywords: accelerated MRI; parallel imaging; iterative image reconstruction; numerical optimisation; machine learning; deep learning.
  DOI: 10.1504/IJMEI.2022.10048343
   
  
• Brain cancer analysis using deep learning architecture on MRI brain image  
  by B. Kannan, S. Karthigai Lakshmi 
  Abstract: Brain cancer diagnosis in the medical images sector without human involvement is a huge complex one. The brain tumour tissue can be detected from the whole brain are extremely difficult. Multi-sequence MRI technology is not standardised in brain tumour segmentation clinical practice and hence, a flexible segmentation process is required which uses all of the available MRI data optimally. The proposed algorithm provides a precise and robust segmentation of tumours, which helps in diagnosis, therapy planning, and risk factor detection. SVM classification and convolutional neural network classification are applied and analysed. The proposed system obtains more accurate predictions.
  Keywords: image processing; MRI images; brain tumour; SVM classification; watershed image segmentation techniques; deep learning.
  DOI: 10.1504/IJMEI.2022.10048344
   
  
• Detection of Parkinson's disease using CNN  
  by M. Kamesh, C. Augustine, D. Sarathy, S. Leopauline, Sheshang D. Degadwala 
  Abstract: Parkinson’s disease can be diagnosed using computer-assisted diagnosis systems based on brain imaging, with the ultimate goal of finding patterns that characterise the disease. In this case, convolutional neural networks (CNNs) have proven to be extremely beneficial. Neurological disease Parkinson’s disease (PD) is characterised by a decrease in the brain’s dopamine-producing neurons. Patients with Parkinson’s disease have difficulty producing speech due to a lack of coordination in the muscles that control breathing, phonation, articulation, and prosody, among other things. Speech analysis can be used by clinicians to objectively assess the severity of Parkinson’s disease in a non-invasive manner. In the LSTM layer, the output is then analysed for important temporal feature relationships. Existing state-of-the-art CNN models are compared to the proposed DenseNet-LSTM model. Training accuracy is 93.75%, testing accuracy is 90%, and validation accuracy is 93.87%, according to the suggested model.
  Keywords: Parkinson’s disease; Parkinson’s speech; empirical mode decomposition; convolutional neural networks; CNNs; Parkinson’s disease; machine learning; meta-heuristics.
  DOI: 10.1504/IJMEI.2022.10048478
   
  
• A review on automated detection and classification of diseases in retinal images  
  by M.C. Padma, Esra'a Mahmoud Jamil Al Sariera, Thamer Mitib Ahmad Al Sariera 
  Abstract: Hypertensive retinopathy (HR) and glaucoma are the most diseases that cause blindness. Early detection of these diseases is very important for ophthalmologists in clinical diagnostic and successful treatment. The identification and diagnosis of glaucoma and HR require segmentation of the normal objects inside retina such as blood vessels and optic disc (OD). This article describes publicly available retinal datasets and an overview of the state-of-the-art for segmenting normal objects in the retina such as blood vessels and the OD, as well as ways for detecting pathologies that affect normal objects in the retina such as glaucoma and HR. The purpose of this study is to develop a professional structure that will familiarise the researcher with the most up-to-date blood vessels and OD segmentation techniques and the classification of HR and glaucoma diseases. Furthermore, we compared the dataset, evaluation metrics, pre-and post-processing steps, segmentation and classification methods and induced results of these approaches.
  Keywords: hypertensive retinopathy; HR; glaucoma; blood vessels; optic disc; OD.
  DOI: 10.1504/IJMEI.2022.10048781
   
  
• Machine learning-based CAD system for thyroid tumour characterisation using ultrasound images  
  by Niranjan Yadav, Rajeshwar Dass, Jitendra Virmani 
  Abstract: The main objective of this paper is to develop an efficient computer-aided diagnosis (CADx) system for the characterisation of thyroid tumours using US images. An efficient CADx system is designed to address speckle noise’s effect on thyroid tumour classification in this work. The analysis has been carried out with 820 thyroid ultrasound images. The CADx system designs were examined based on original and despeckled images to compare the texture and morphological features. The extracted features have been used to design five CADx systems, namely: 1) PCA-KNN; 2) PCA-PNN; 3) PCA-SVM; 4) SFS-SVM; 5) LS-ANFC algorithms. The results illustrate that a CADx system based on the PCA-SVM algorithm with (LBP + ZRM) features yields optimal performance for the characterisation of thyroid tumour ultrasound images.
  Keywords: thyroid ultrasound images; local binary pattern; LBP; Zernike features; support vector machine; sequential feature selection; SFS.
  DOI: 10.1504/IJMEI.2022.10049164
   
  
• Analysis, design and implementation of electronic navigation device for visually impaired persons  
  by Abiodun O. Ogunsanya, Omini E. Okoi 
  Abstract: In this paper, we describe an electronic navigation system, a wearable device intended for obstacle detection and navigation purposes for visually impaired persons. Three ultrasonic sensors, a passive infrared sensor, a programmed microcontroller, GPS module, memory card, audio stimuli feedback, and vibro-tactile feedback comprise the inexpensive, commodity hardware, were connected in a closed circuit. They were used to detect barriers at a distance of up to 1 m. The passive infrared sensor was utilised to detect both animate and inanimate impediments. Initial results of the experiment demonstrate that using the wearable navigation system, the percentage collision rate was reduce that is, 90.1% reduction, 50% reduction in response time, and 98% of variance in distance detection using regression analysis compared to conventional guide care. The device shows dependable response to obstacle detection with minimal error. This demonstrates that wearable navigation device is safe and has the potential to improve quality of life of visually impaired persons.
  Keywords: visually impaired; obstacle detection; wearable navigation device; convectional guide care; microcontroller.
  DOI: 10.1504/IJMEI.2022.10049168
   
  
• Deep learning approaches in detection of COVID-19 using X-ray images  
  by Shibili Said, Mredhula Lokanathan 
  Abstract: We all have seen how badly COVID-19 had affected our lives. The utilisation of deep learning in analysing covid is a promising research area. This paper brief on the utilisation of deep learning in analysing covid X-ray. A Convolutional Neural Network (CNN), a deep learning algorithm is utilised here, to study the detection of covid X-rays. The literature shows several successful deep learning models in detection of COVID-19. In this paper, we have implemented shallow layered simple CNN and deep layered CoroNet (which is taken from literature) using three different dataset. This paper would give a brief picture of deep learning in covid classification.
  Keywords: deep learning; covid X-rays; convolutional neural network; CNN.
  DOI: 10.1504/IJMEI.2022.10049172
   
  
• Analogy of cervical malignancy through Inception V3 and Xception network of CNN  
  by K. Hemalatha, N. Kasthuri, N.S. Kavitha, T. Jamuna, K. Kanchana 
  Abstract: Cancer is a disease formed by the abnormal growth of cells and if it not treated in its early stage it spreads to other parts of body. There are more than hundred types of cancer available in the world. An effective method of testing is needed to diagnose the presence or absence of disease, monitor progress of the cancer, and evaluate the treatment’s effectiveness. Cervical cancer ranks fourth among the cancers afflicting women worldwide. To overcome the aforementioned problem deep learning techniques are used for automatically diagnosing the disease from Pap smear images. The proposed model is evaluated on SIPAKMED dataset. CNN-based architectures such as Inception V3 and Xception were used to classify the cervical cells and their accuracy was ascertained. The performance measures such as precision, recall and sensitivity are calculated. The obtained results concluded that CNN pre-trained model Xception achieved the higher classification rate of 95.99%.
  Keywords: cervical cancer; SIPAKMED; Xception; Inception V3; deep learning.
  DOI: 10.1504/IJMEI.2022.10049258
   
  
• Use of a neuro-fuzzy technique to predict complete Rockall score in patients with upper gastrointestinal bleeding  
  by Zhaleh Ghobadi, Somayeh Saraf Esmaili 
  Abstract: This study, using a neuro-fuzzy technique tried to provide an intelligence model for predicting the complete Rockall score of the patients with upper gastrointestinal bleeding. The data related to 350 patients with upper gastrointestinal bleeding who were hospitalised in the public hospital in Iran have been used. The 30% of the data were randomly selected and used as test data, and the others were also applied as training data. Clinical Rockall and Blatchford scores as inputs and complete Rockall score as the output were considered. To evaluate the model performance, conventional criteria were calculated. The accuracy of the model was compared with the accuracy of the artificial neural network tool. According to the results, proposed model has higher accuracy and efficiency than the artificial neural network and it seems to be used as a useful tool. Accuracy, cheap, high speed and being new are some benefits of the presented model.
  Keywords: upper gastrointestinal bleeding; Rockall; Blatchford; artificial intelligence; neuro-fuzzy system; artificial neural network.
  DOI: 10.1504/IJMEI.2022.10049259
   
  
• Auditory model system to recognise Alzheimer's diseases: speech signal analysis  
  by Ravi Kumar, R. Prabha, B. Kannan, T.J. Nagalakshmi, Sheshang D. Degadwala 
  Abstract: Alzheimer’s disease is a catch-all phrase for a variety of illnesses, including numerous neurodegenerative disorders. A century ago, neurosyphilis-caused dementia was the most frequent cause of dementia in developed nations, but Alzheimer’s disease (AD) is today the most common cause. Dementia might still be difficult to diagnose because of a number of barriers. Early symptoms overlap with other disorders, and the potential of several, or mixed, aetiologies are just a few of the factors that contribute to a wide range of possible outcomes. Because language diminishes along with neurodegeneration in Alzheimer’s disease, it is a significant source of clinical information. As a result, data on speech and language have been intensively investigated in relation to its diagnosis. Text characteristics extracted from the transcripts are used to detect AD using an SVM classifier. However, the accuracy of automatic assessment falls as WER rises, the two are very weakly associated (0.31).
  Keywords: Alzheimer’s disease; auditory model system; wavelet transform; gammatone filter.
  DOI: 10.1504/IJMEI.2022.10049260
   
  
• Brain image enhancement and segmentation using anatomically constrained neural networks  
  by P.S. Arthy, A. Kavitha 
  Abstract: Brain image segmentation is one of the processes that take the most time and is the most complicated to do in a therapeutic scenario. The essential principles and features of medical image segmentation based on deep learning are presented. MRI-based medical image classification issues are addressed in this study using a histogram and time-frequency differential deep (HTF-DD) technique. The following are the stages of the proposed approach’s construction. An unsupervised training procedure is used to build a deep convolutional neural network (CNN), which then outputs standardised improved pre-processed features for data extraction. Secondly, a set of time-frequency characteristics is derived from medical images using the time signal and the frequency signal. The last step is to develop an effective model based on differential deep learning for classifying objects. Multi-modal brain data sets and public standards are used to illustrate the applicability of our methodology.
  Keywords: neural network; image processing; brain image enhancement; segmentation; anatomically constrained neural networks; ACNNs; histogram and time-frequency differential deep; HTF-DD; convolutional neural network; CNN.
  DOI: 10.1504/IJMEI.2022.10049261
   
  
• Detection and classification of arrhythmia disorders using machine learning algorithm  
  by P. Ramani, S. Sugumaran, Manoharan Nivethitha Devi, T.J. Nagalakshmi, G. Annapoorani 
  Abstract: A recent study by the United Nations Agency (World Health Organization) reported that 17.9 million people died due to cardiovascular disease, and it is increasing exponentially. Furthermore, it was also reported that it was highly difficult to recognise the sickness and dictate the relevant care in a timely manner. For analysis, a user data file for cardiopathy prediction that contains parameters such as gender, age, kind of pain, force per unit area, hyperglycaemia, and so on has been considered. The approach entails determining the correlations between the numerous properties of the data file using regular processing techniques and then treating the attributes appropriately to forecast the likelihood of cardiopathy. This article endeavours at probing methodised data-mining techniques such as NB classifier, random forest classification, decision tree in addition to support vector machine. These machine learning approaches require less time to anticipate sickness with a high degree of accuracy. The proposed algorithm provides 91.2% recognition rate than SVM and decision tree classifier.
  Keywords: support vector machine; SVM; NB classifier; random forest; RF; arrhythmia disorders; decision tree.
  DOI: 10.1504/IJMEI.2022.10049587
   
  
• Feature analysis for fundus image classification of multi-retinal diseases  
  by Widhia K.Z. Oktoeberza 
  Abstract: Retinal diseases affect the vital eye tissue, which can reduce eye vision and even cause blindness if left undiagnosed and untreated. Some retinal diseases can be prevented and even treated properly to return the lost vision by conducting early detection. A scheme to classify multi-retinal diseases is proposed in this study; specifically diabetic retinopathy (DR), age-related macular degeneration (ARMD), and media haze (MH). The process is starting by extracting some features consisting of statistical and texture features, which are undergone in 500 fundus images taken from the RFMiD dataset. Thereupon, these features were classified based on the MLP classifier. Based on that classification process, the accuracy rate of DR, ARMD, and MH classifications achieved 84.2%, 93.2%, and 89.4%, respectively. These achievements show that the proposed scheme effectively classifies multi-retinal diseases and has the potential to assist ophthalmologists in early detecting the appearance of retinal diseases for preventing the worst effect.
  Keywords: retinal diseases; feature analysis; image classification; fundus images.
  DOI: 10.1504/IJMEI.2022.10049694
   
  
• A CNN and spatial tract-based statistics-based approach for the diagnosis of Alzheimer’s disease  
  by Latifa Houria, Noureddine Belkhamsa, Assia Cherfa, Yazid Cherfa 
  Abstract: The prevalence of Alzheimer’s disease (AD) is growing dramatically each year, making it critical to find efficient strategies to detect the disease early on and prevent its progression. In this work, we present an approach combining tract-based spatial statistics (TBSS) and convolutional neural network (CNN) to classify the AD and mild cognitive impairment (MCI) from a cognitively normal (CN) subject using the diffusion tensor imaging (DTI). The TBSS was applied to generate the WM skeleton from the two DTI maps fractional anisotropy (FA) and mode of anisotropy (MO). The CNN is trained first on FA-Skeleton and MO-Skeleton and then fine-tuning on FA and MO relevant slices. This combinatory procedure achieved a higher result and represents a powerful diagnostic tool.
  Keywords: diffusion tensor imaging; DTI; Alzheimer’s disease; convolutional neural network; CNN; tract-based spatial statistics; TBSS; fractional anisotropy; mode of anisotropy.
  DOI: 10.1504/IJMEI.2022.10049862
   
  
• Classification of brain MRI using hypercolumn technique with convolutional neural network  
  by K. Kavin Kumar 
  Abstract: The purpose of this study is to classify brain tumours. When abnormal cells grow within the brain, a brain tumour develops. Cancerous (malignant) tumours and benign (non-cancerous) tumours are the two basic forms of tumours. In this regards an algorithm is developed to classify the tumour and non-tumour. The main focus is given to the hypercolumn implementation along with VGG-16 and ALEX-NET. The datasets were taken from the Kaggle and real brain data from Johnson’s MRI and it consists of tumour and non-tumour. The obtained result identifies whether the person is having a tumour or not. The developed algorithm is having an accuracy of 94.5% for VGG-16 with hypercolumn and 91.2% for ALEXNET with hypercolumn.
  Keywords: tumour; non-tumour; hypercolumn; ALEXNET; VGG16; MRI; malignant; benign.
  DOI: 10.1504/IJMEI.2022.10049911
   
  
• Automatic knee anterior cruciate ligament torn diagnosis using CNN-XGBoost  
  by Kamel H. Rahouma, Ahmed Salama Abdel Latif, Kadry Ali Ezzat 
  Abstract: The knee joint is very important for everyone as it helps us in movements, which is essential for everyone. One of the most diseases that injure the knee is the anterior cruciate ligament (ACL). This work has developed a computer aided diagnosis (CAD) system for examining the given knee magnetic resonance imaging (MRI) image and automatically determining if there is a torn in ACL or not. The region growing based segmentation algorithm is used to get the region of interest (ROI) from MRI image, e.g., extract ACL region from knee image then CNN-XGBoost model is used for knee ACL classification. The model is divided into two main parts: the first part extract the feature uses CNN and the second part using XGBoost for feature classification. The designed model gives us an accuracy of 91%.
  Keywords: deep learning; CNN; XGBoost; knee ACL.
  DOI: 10.1504/IJMEI.2022.10049912
   
  
• Analysis and trends of COVID-19 in Italy  
  by Cinzia Muriana, Giovan Battista Vizzini 
  Abstract: SARS-CoV-2 is impacting the public health-system worldwide and requires policies to address the demand for additional capacity. Monitoring its spread allows the identification of alarm signals useful for scaling up resources and reacting to the pandemic. In Italy, starting with the identification of the first patient, the Protezione Civile has published a range of indicators as open data, which has supported the country’s government in discovering trends and in setting-up targeted measures for preventing the spread of the virus and controlling the speed of transmission. This paper analyses these indicators from February 2020 to June 2021 and provides insights for healthcare managers.
  Keywords: SARS-CoV-2 outbreak; SARS-CoV-2 predictors; COVID-19 response; outbreak monitoring; Italy.
  DOI: 10.1504/IJMEI.2022.10049913
   
  
• Design of automated computer aided diagnosis system to predict diabetic retinopathy based on EfficientNet  
  by S. Sathiya Devi, K. Vignesh, V. Raguvaran 
  Abstract: The World Health Organization (WHO) recognises that diabetic retinopathy (DR) is one of the rising healthcare problems in the world which leads to vision loss if left untreated. In this paper, an automated computer aided diagnosis (CAD) system for DR is implemented based on EfficientNet. Initially, image pre-processing is performed by smoothing it with median filter and converting into grey scale image. Then size, colour and shape normalisation are carried out. To increase the volume and to solve the data imbalance issue flipping, rotation, zooming and distortion operations are performed. The CAD system diagnoses the severity levels of DR from fundus images by exploring feature extraction based on EfficientNet B0 model and classification with XGBoost classifier. It is experimented with Indian Diabetic Retinopathy Image Dataset (IDRiD) and experimental result reveals that, the combination of EfficientNet B0 and XGBoost produces better classification accuracy when compared with other convolutional neural network (CNN) models.
  Keywords: diabetic retinopathy; EfficientNet B0; XGBoost classifier; computer aided diagnosis system; healthcare; convolutional neural network; CNN.
  DOI: 10.1504/IJMEI.2022.10050478
   
  
• Solving moderated mediation model using interval data on fuzzy elevation  
  by A. Akilbasha, R. Vanitha, D. Kalpanapriya 
  Abstract: A variable that grounds mediation between dependent and independent variables. These mediator variables take vital parts in the analysis of data which entails numerous variables, exclusively when the dependent and independent variables are affected by other variables. Thus, mediation analysis requires all areas which need regression analysis, especially in psychology, industry, teaching, production area, etc. Mediation has been projected in multiple studies. On the other hand, at times it is more logical to articulate the fuzzy theory data when the variables are not purely distinct. Normally, to articulate the attitude of a persona bad, moderate, good use fuzzy numbers than using crisp numbers. This paper explains the model of fuzzy moderated mediation using interval data. Also, to find the total, direct and indirect effects psychological data have been applied when the mediator, moderator, and confounding variable subsist.
  Keywords: mediation; moderation; moderated mediation; fuzzy mediation; interval data.
  DOI: 10.1504/IJMEI.2022.10049914
   
  
• Cardiac arrhythmia classification of imbalanced data using convolutional autoencoder and LSTM techniques  
  by Rekha Rajagopal, V. Shyam Kumar 
  Abstract: Cardiovascular diseases (CVD) can be identified by medical professionals with the help of electrocardiogram (ECG) signals. The ECG signals shows the heart rhythm and any irregularity in heart rhythm is called arrhythmia. The arrhythmias can be broadly classified into five categories: 1) class N; 2) class S; 3) class V; 4) class F; 5) class Q. The proposed research work automatically categorises the ECG beats into one of the five classes using long short-term memory (LSTM). The ECG waveform is divided into individual ECG beats and is provided as input to the convolutional autoencoders. The compressed representation of the encoder is used as features for further classification by LSTM. The class imbalance problem in the dataset is overcome using ADASYN technique. The proposed research work gives an overall accuracy of 99.12%.
  Keywords: arrhythmia; long short-term memory; LSTM; autoencoder; ADASYN; deep learning; disease classification; convolutional neural network; CNN.
  DOI: 10.1504/IJMEI.2022.10049967
   
  
• Epilepsy detection and classification based on the contour maps of brain MR images  
  by H.S. VinayKumar 
  Abstract: A new method of Epilepsy detection and classification of the degree of affliction is presented. The proposed method is based on the information generated by the filled contour maps of the brain MRI. The result is independent of the size and orientation of the target image. Distribution of multiple intensity levels in an MR image is generated by the proposed contour map segmentation technique. Disease afflicted regions are segmented using this technique. The epilepsy diagnosis is carried out by classifying MR images as healthy and diseased. The diseased ones are further classified into three sub-classes of affliction, namely, mild, medium and severe. The classification is based on the area of the top level segment relative to the total area of the non-skull region. In the proposed method, the classification error is found to be between 5% to 10%.
  Keywords: contour map segmentation; skull-stripping; top level segment; area of non-skull region; filled contour map; contour level list.
  DOI: 10.1504/IJMEI.2022.10050095
   
  
• A secure health monitoring system based on fog to cloud computing  
  by Hafida Saidi, Nabila Labraoui, Ado Adamou Abba Ari 
  Abstract: Nowadays, the elderly can receive care in their home and enable physicians to follow their diseases in real-time. However, these technologies suffer from several issues like security and privacy-preserving data challenges. In this paper, we proposed a HIPAA-compliant framework that enables security and privacy-preserving medical data based on fog-to-cloud (F2C) computing. Our aims are to define a system that solves the privacy and security issues with remote elderly monitoring. The F2C infrastructure is used to provide better security of medical data and allow a real-time diagnosis of the elderly. Furthermore, F2C combines the benefits of cloud and fog computing such as providing permanent storage, reducing computation load and data transmission delay, and enhancing the security challenges. Simulation results suggest that F2C technology delivers better performance in terms of latency, cost, and energy consumption.
  Keywords: elderly healthcare; wearable sensors; fog to cloud computing; AES-ECC encryption; internet of medical things; IoMT.
  DOI: 10.1504/IJMEI.2022.10050253
   
  
• An advanced manta ray search optimisation with likelihood marginal classification mechanism for ECG arrhythmia detection  
  by Budidi Udaya Kumar, Jaidhan Beera John, Bharath Kumar Reddy Soma Venkata, Ummadisetty Sreenivasulu 
  Abstract: The proposed work objects to develop an advanced optimisation-based classification system for identifying the arrhythmia disease with increased accuracy and reduced error rate. The optimal features are selected using the manta ray search optimisation (MRSO) technique. The likelihood marginal classification (LMC) approach is employed to predict the classified label as whether normal or disease affected. During analysis, the different types of performance measures are used to evaluate the results of the proposed approach. Also, the obtained values are compared with the existing techniques for showing the effectiveness of the proposed model.
  Keywords: electrocardiogram; ECG; cardiovascular disease; manta ray search optimisation; MRSO; likelihood marginal classification; LMC.
  DOI: 10.1504/IJMEI.2022.10050386
   
  
• Reconstruction of a 3D medical image from pre-processed 2D DICOM slices : clinical application  
  by Kamal Halloum, Hamid Ez-Zahraouy 
  Abstract: This paper presents two techniques, histogram equalisation (HE) and contrast limited adaptative histogram equalisation (CLAHE), for contrast enhancement of 2D magnetic resonance imaging (MRI) slices in order to reconstruct them as 3D images. These powerful techniques allow the maximum possible contrast to be established without ultimately affecting medical interpretation, based on a comparative study of three 3D images constructed by the slices: real, enhanced only by HE and those processed by CLAHE after HE. We have experimentally demonstrated that the CLAHE technique is effective in improving contrast and reducing noise amplification especially for medical images.
  Keywords: histogram equalisation; CLAHE; 3D medical image; DICOM image; image processing.
  DOI: 10.1504/IJMEI.2022.10050387
   
  
• Detection of adverse drug reactions from online health communities’ data: a case study of anti-epileptic drugs  
  by Anwar Ali Yahya 
  Abstract: This paper investigates the problem of detecting adverse drug reactions of anti-epileptic drugs from patients’ reviews in online health communities. A lexicon-based methodology is proposed and applied to a dataset of patients’ reviews collected from two online health communities. The dataset is cleaned and the adverse reactions of anti-epileptic drugs are extracted with the aid of consumer health vocabulary and a lexicon of adverse drug reactions. A proportional reporting ratio is then applied to quantify the correlation between each drug and adverse reactions and thus identify the adverse reactions of each drug. The results are validated quantitatively against a database of adverse drug reactions, called side effect resource, and qualitatively against the extant knowledge related to the common adverse reactions and drug-drug similarities of anti-epileptic drugs. The validation results provide evidences on the effectiveness of the proposed methodology and the validity of online health communities’ data for adverse drug reactions detection.
  Keywords: adverse drug reaction detection; pharmacovigilance; anti-epileptic drugs; AEDs; data mining; online health communities.
  DOI: 10.1504/IJMEI.2022.10050418
   
  
• Segmentation of the human spinal cord using U-Net architecture  
  by S. Kumarganesh, Muzammil Hussain, H. Shaheen, S. Anthoniraj, M. Somaskandan, C. Sivakumaran 
  Abstract: When attempting to assess spinal cord atrophy caused by a variety of disorders, the first step that must be taken is to segment the spinal cord contour. A tumour of the spinal cord is an abnormal development of cells that may occur anywhere in or around the spinal cord. The process of locating tumours in the spinal cord is a very important one. It is difficult to identify the tumour with MRI due to the irregular form of the spinal cord. The model begins by locating the spinal cord, after which it creates the bounding box coordinates. Our technique is validated using four separate clinical datasets. The results of the experiments that used a unique segmentation strategy that was dependent on MRI images reveal that the algorithm that was presented for the system delivers a higher level of accuracy when compared to the other algorithms that are already in use.
  Keywords: spinal cord segmentation; bounding box; U-Net architecture; deep learning.
  DOI: 10.1504/IJMEI.2022.10050461
   
  
• Telemetry drug delivery techniques and design. A review  
  by Maham Sarvat, Suhaib Masroor, Muhammad Muzammil Khan 
  Abstract: Drug delivery systems (DDS) are developed for the controlled delivery of therapeutic agents. After administration, the DDS releases the active agents, and subsequently, the bioactive molecules are transported across various biological barriers to reach the site of action. Scientists have made a major contribution in understanding the role of various physiological obstacles for the efficient delivery of drugs in the circulatory system and the diffusion of drugs across cells and tissues. This review discusses and compares the drug delivery methods, drug delivery techniques, telemetry drug delivery, mechanical structure of the drug delivery system, and multiple syringes for DDS and also nano-technology drug delivery. The review leads to the conclusion that the wireless drug delivery method overcomes almost all of the drawbacks of the other methods and thus deserves the most attention in future biomedical research.
  Keywords: telemedicine; biomedical instrumentation; syringe pump; drug administration; drug delivery systems; DDS.
  DOI: 10.1504/IJMEI.2022.10050462
   
  
• Early knowledge-driven prognostic reasoning model using effective data analytics approach  
  by Rithesh Pakkala PermankiGuthu, Shamantha Rai Bellipady, Srinidhi Rai, Tirthal Rai 
  Abstract: Diabetes is a lifestyle disorder. The accomplishment of early knowledge of diabetes can enhance the treatment effectiveness. Data analytics techniques are widely used to gain early knowledge of the disease. In this research, the prognostic reasoning model is designed to identify the significant features that lead to the detection of early knowledge on diabetes using an effective data analytics approach. For the analysis of diabetic knowledge, different classifiers, namely decision tree, support vector machine, neural network, and random forest are used. The experiment depicts that random forest performs superior to other classifiers in the early prediction of diabetes with an accuracy of 96% and thus may be valuable in assisting doctors in making patient care.
  Keywords: early knowledge; prognostic reasoning; data analytics; diabetes.
  DOI: 10.1504/IJMEI.2023.10050804
   
  
• Deep learning-based malignancy prediction in thyroid nodules  
  by L. Mohana Sundari, M.S. Maharajan, T. Senthil Kumar, Leo John Baptist Andrews 
  Abstract: Although the vast majority of thyroid nodules are non-cancerous, determining whether or not a nodule is cancerous may be a difficult and time-consuming process that often involves unnecessary surgical events. In addition, we discussed the process of developing a model that might anticipate the presence of cancer in thyroid nodules by including a number of the core demographic and ultrasound parameters. A combined sensitivity and specificity score was used to assess the diagnostic performance, and their accuracy was compared to that of radiologists. The comparison between model prediction and expert evaluation reveals the benefit of our approach over human judgement in predicting thyroid nodule malignancy. The results of the experiments show that the suggested algorithm performs better. Nodules of TI-RADS category 4 were used. The area under the receiver operating characteristic curve in the validation dataset was 0.92 (with accuracy of 0.70, sensitivity of 0.81 and specificity of 0.58).
  Keywords: thyroid; AI; deep learning; medical imaging; deep learning; CNNs; preoperative diagnosis.
  DOI: 10.1504/IJMEI.2022.10050829
   
  
• Empirical wavelet decomposition of photoplethysmographic signal for hypertension risk stratification and detection of diabetes mellitus using machine learning techniques  
  by Muzaffar Khan, Bikesh Kumar Singh, Neelamshobha Nirala 
  Abstract: Hypertension (HT) is a leading risk factor for cardiovascular disease (CVD), and the overlap of diabetes mellitus (DM) with hypertension can lead to severe complications. Presently, the diagnostic method for detecting hypertension and DM is unsuitable for large-scale screening. The proposed model uses a statistical feature extracted by decomposing the PPG signal into a sub-band signal using Empirical wavelet transform (EWT), a comparative study conducted between various soft and hard computing classification models. The highest accuracy achieved by sequential neural network for the three categories, namely normal (NT) vs. prehypertension (PHT), NT vs. hypertension type 1 (HT-I), NT vs. hypertension type 2 (HT-II) in terms of F1 scores is 78.9%, 91.2% and 94%, respectively and F1 score of 97.9% for detection of DM-II patients. We conclude that soft computing techniques such as deep learning neural networks have shown superior performance compared to hard computing techniques. Furthermore, features selected using a hybrid feature selection technique were found to improve the classifier’s performance. The main advantage of the proposed model that uses a decomposition technique is found to be more immune to noisy PPG signals, overcoming the limitation of the morphological-based model.
  Keywords: hypertension; diabetes mellitus; photoplethysmographic; empirical wavelet transform; Hilbert transform ensemble classifier; deep learning neural network.
  DOI: 10.1504/IJMEI.2022.10050838
   
  
• Deep neural networks for medical image segmentation: geodesic distance transform  
  by P. Jenifer Darling Rosita, W. Stalin Jacob, R. Kalpana, T. Cynthia Anbuselvi 
  Abstract: The segmentation of medical images aids in managing the dose of medication, as well as the dosage of exposure to radiation, limiting the development of diseases like tumours, and monitoring the progression of diseases like cancer. The process of segmentation involves the division of a picture into distinct regions that each includes fragments of pixels that have similar characteristics. The regions should have a strong link to the items or elements of interest depicted in the picture in order to be expressive and useful for image analysis and interpretation. A proposal is made for an interactive framework that takes a deep learning approach. P-Net is the first stage of the framework, and it is used to produce an initial automated segmentation. The second stage is where the framework is implemented. This interaction is included in the input of the R-Net.
  Keywords: image segmentation; deep learning; brain tumour; datasets.
  DOI: 10.1504/IJMEI.2022.10050839
   
  
• Detection of diabetic retinopathy severity from fundus images: DCNN  
  by T.Senthil Kumar, R. Muthalagu, L. Mohana Sundari, M. Nalini 
  Abstract: Diabetes retinopathy is a frequent diabetic complication that damages the retina and, if left untreated, may lead to blindness. The exponential rise in the number of diabetics throughout the globe has resulted in an equivalent rise in the number of diabetic retinopathy (DR) patients, one of the most serious consequences of diabetes. The goal of this research is to develop a hybrid solution approach for identifying diabetic retinopathy using retinal fundus pictures. The process of retinal vascular segmentation is critical for detecting a variety of eye disorders, such as the effects of diabetes on the eyes, also known as diabetic retinopathy. Morphologically based operations were used for the autoextraction of retinal blood vessels. Wavelet decomposition and back propagation neural networks were used to extract retinal vascular characteristics and evaluate the dataset that was used for this article. Morphologically based operations were also used for autoextraction of retinal blood vessels.
  Keywords: diabetic retinopathy; fundus images; retina; deep learning; image processing.
  DOI: 10.1504/IJMEI.2022.10050840
   
  
• Detection and classification of Alzheimers using super-resolution algorithm and convolutional neural network  
  by T. Senthil Kumar, Ashok Vajravelu, R. Muthalagu, P. Sri Latha 
  Abstract: On the basis of data obtained from brain imaging, a number of different machine learning (ML) methods may be used to categorise Alzheimer’s disease (AD). Convolutional neural networks have been suggested for the classification of Alzheimer’s disease based on anatomical MRI in more than 30 different studies. Since the frameworks and implementation details of many researchers aren’t available to the public, it makes them difficult to replicate. This article extracts the green channel initially, which is further enhanced by using super resolution algorithm. Convolutional neural network is applied to the contrast-enhanced image. We used CNN and T1-weighted MRI to broaden open-source solution for Alzheimer’s disease categorisation. Preprocessing, classification, and evaluation techniques for deep learning are included in the framework as well as tools for converting ADNI, AIBL, and OASIS data to the standard of the BIDS format. By combining deep learning with radionics, the accuracy of Alzheimer’s disease diagnosis is increased.
  Keywords: image classification; convolutional neural network; CNN; Alzheimer’s disease classification; magnetic resonance imaging; MRI.
  DOI: 10.1504/IJMEI.2022.10050896
   
  
• Breast cancer diagnosis by hybrid fuzzy CNN network  
  by W. Stalin Jacob, P. Jenifer Darling Rosita, M. Sri Geetha, P. Jagadeesh, Sivakumaran Chandrasekaran 
  Abstract: Breast cancer is a common gynaecological ailment that affects women all over the world. Early identification of this disease has been shown to be extremely beneficial in terms of therapy. Mammographic pictures are analysed in this article utilising image processing methods and a pipeline structure to see whether they contain malignant tumours, which are subsequently categorised. The SVM classifier is used for classification, and it is fed by the characteristics that have been picked. It is supported by a number of kernel functions. This differs from standard machine learning classification and optimisation strategies, and it is shown in a unique manner. The outcomes of the actualised computer-aided diagnostic (CAD) learning process are analysed in order to determine whether or not it was successful. The BCDR-F03 dataset is evaluated, as well as the: 1) local mammographic dataset; 2) colony optimisation-based multi-layer perceptron (ACO-MLP) dataset.
  Keywords: breast cancer; deep learning; convolution neural network; CNN; prediction; benign and malignant; computer-aided diagnostic; CAD.
  DOI: 10.1504/IJMEI.2022.10050897
   
  
• Robust liver segmentation using marker controlled watershed transform  
  by Mohammad Anwarul Siddique, Shailendra Kumar Singh, Moin Hasan 
  Abstract: The liver is the body’s largest organ, and it is largely responsible for metabolism and detoxification. In computer vision-based biomedical image analysis, liver segmentation is a critical step in detecting liver cancer. Due to the complicated structure of abdominal computed tomography (CT) images, noise, and textural differences across the image, liver segmentation is a key task that results in under-segmentation and over-segmentation. This paper uses a marker-based watershed transform to segment the liver in abdominal CT images. The double stage Gaussian filter with texture and contrast enhancement (DSGFTCE) is used to improve image quality at the pre-processing stage. The performance of the proposed segmentation is assessed using various performance evaluation metrics such as dice score (DS), volume overlapping error (VOE), Jacquard index (JI) and relative volume difference on LiTS dataset. The performance comparison with previous state of arts shows that proposed liver segmentation scheme provides better results (DS = 0.968, VOE = 0.089, JI = 0.9379, RVD = 0.09) compared with existing techniques.
  Keywords: liver segmentation; contrast enhancement; texture smoothening; watershed transform; Gaussian filtering; computer tomography.
  DOI: 10.1504/IJMEI.2022.10050979
   
  
• Trimester analysis for detecting abnormalities in foetal growth based on femur length using LabVIEW  
  by D. Ruban Thomas, V. Vasanth, R. Kishore, B. Kishore Kumar 
  Abstract: Foetal development characteristics are used to determine the effects of various maternal traits and identify people who are more likely to have adverse outcomes. Foetal development features were measured during each trimester of pregnancy and were impacted by several mothers socio-demographic and lifestyle factors. Ultrasound measures of the foetus are crucial during pregnancy. The stability of foetal growth features can be assessed using tracking analyses throughout pregnancy. The length of the femur, the long bone in the thigh, is one of the markers used to monitor foetal growth and health. Previous research concentrated on birth outcomes compared to the foetal development, during pregnancy. In this work, the femur length is measured using LabVIEW. An ultrasound finding of short femur length may signal the need for additional testing to rule out illnesses. Femur length is one of the several factors for determining a babys health.
  Keywords: Laboratory Virtual Instrument Engineering Workbench; LabVIEW; virtual instruments; VI; trimester; ultrasound; ultrasonography; foetal growth; femur length; pregnancy.
  DOI: 10.1504/IJMEI.2022.10051334
   
  
• Analyses of non-steroidal anti-inflammatory drug induced GI bleed related hospitalisation discharges from 2016 to 2018 in the USA  
  by Chancey Sweeney, Shankar Srinivasan, Dinesh P. Mital, Riddhi Vyas 
  Abstract: This study determines the predictors of gastrointestinal (GI) bleeds induced by NSAIDs and their relation to length of hospital stay and total charges. A descriptive analysis showed the highest incidence as non-Hispanic White, older adult patients with variation in household income. Risk factors include Helicobacter pylori, alcohol, and smoking. The overall prediction of study outcomes showed associations between independent and dependent variables such as age, race, and gender, in relation to length of hospital stay and total charges. A high influence was detected with interaction of predictors such as health and socio-demographic characteristics, like advanced age, gender, race, and smoking.
  Keywords: non-steroidal anti-inflammatory drug; NSAID; gastrointestinal bleed; NSAID induced; GI bleed; hospitalisation discharges.
  DOI: 10.1504/IJMEI.2022.10051360
   
  
• A deep analysis of chronic kidney disease for early detection using machine learning classifiers  
  by Saurabh Pal 
  Abstract: All patients who have suffered by chronic kidney disease (CKD) are not easily identified due to symptoms on early stage. The main objective of this research paper is to develop a CKD prediction model which can give better accuracy as compared to other studies. In this paper, we have organised CKD datasets from UCI machine learning repository. Most significant CKD features are eliminated by recursive feature elimination techniques. We have trained base classifiers RF, K-NN, MLP and SVM on 70% disease dataset and test on 30% dataset. Bagging and voting ensemble methods are used to enhance the prediction model. The proposed bagging ensemble model outperformed the other classifiers by achieving 97% accuracy.
  Keywords: chronic kidney disease; CKD; recursive feature elimination; RFE; random forest; K-nearest neighbour; multilayer perception; support vector machine; voting and bagging ensemble classifier.
  DOI: 10.1504/IJMEI.2022.10051462
   
  
• Developments in technology assisted gait analysis in post-knee arthroplasty  
  by Sumit Raghav, Anshika Singh, Shashwat Pathak, Suresh Mani, Mukul Kumar 
  Abstract: Early monitoring in knee arthroplasty is a critical issue to tackle deviation from expected healing, patient satisfaction and ensuring quality of life. There are various methods suggested and implemented over years with varied degree of performance. This paper presents a relevant review of technology assisted gait analysis in knee arthroplasty. The systematic search revealed 272 studies, of which 13 were added retrospectively through reference screening of the included articles. After title and abstract screening, only 20 studies were included in this review. This review paper provides a comprehensive overview of applications of technology assisted gait analysis to monitor and quantify the status of waking. There is moderate-quality of evidence showed technology-assisted; in particular, sensor-based technology, motion sensors and motion analysis results in a statistically significant improvement in monitoring of gait parameters.
  Keywords: gait analysis; knee arthroplasty; gait parameters.
  DOI: 10.1504/IJMEI.2022.10051736
   
  
• The new development in the lower limb exoskeleton - a review  
  by Jun-Yi Ge, Maysam F. Abbod, Jiann-Shing Shieh 
  Abstract: The lower limb exoskeleton is a human machine interaction system which is designed to help users perform movements involving the lower limb by providing assistive torque. This technology has been proposed and developed in recent years. It is a comprehensive application of biomechanics, automatic control, information science and so on. In this review, we summarised the characteristics of exoskeletons that have emerged in the last twenty years. And at the end of the paper, we propose some possible solutions of the existing exoskeleton problems. This may be a guideline for further research on lower limb exoskeleton.
  Keywords: lower limb; exoskeleton; biomechanics; automatic control.
  DOI: 10.1504/IJMEI.2022.10051870
   
  
• mUNET: glioma segmentation with delineation of tumour sub-regions using optimised architecture of UNET  
  by Sonal Gore, Jayant Jagtap 
  Abstract: Glioma tumour is aggressive due to infiltrative nature, that exhibits unpredictable shape, size, morphology. Therefore, its manual segmentation from MRI remains challenging task. Study presents automated segmentation using T2-FLAIR data of BRATS challenge by proposing modified UNET (mUNET) with optimised architecture to segment tumour in enhancing, necrosis and edema sub-regions. Work was evaluated using six test cases by proposing slight modifications in UNET. Experimentation has gained comparable results with slightly better accuracy of 99.39% and loss of 0.0054, as compared to original UNET. Moreover, test cases were capable to complete segmentation at speed of 0.713 milliseconds per image.
  Keywords: brain tumour; glioma; MRI; FLAIR; segmentation; deep learning; convolutional neural network; modified UNET; mUNET: BRATS data; tumour subregions.
  DOI: 10.1504/IJMEI.2022.10051871
   
  
• Machine learning-based framework for early prediction of diabetes  
  by Salliah Bhat Shafi, Venkatesan Selvam, Gufran Ahmad Ansari 
  Abstract: The recent advancements in technology have changed the landscape of healthcare. With changes in lifestyle and rise in living standard diabetes remains leading cause of death globally. Prediction of diabetes using machine learning algorithms (MLA) for early prediction is need of the hour. However, it is still in its nascent stage. The goal of this study is to employ significant features of machine learning algorithms to the prediction of diabetic and to get the best results which are compared to clinical outcomes. Using predictive analysis, the suggested strategy focuses on choosing the features that aid in the early detection of diabetes. The result shows that the support vector machine (SVM) algorithm has the highest accuracy of 99.349%as compared to naive Bayes which is 98.95%. In order to improve classification and accuracy, this research also normalises the selection of suitable features in the data.
  Keywords: machine learning; naive Bayes; support vector machine; SVM; prediction; diabetes; patients.
  DOI: 10.1504/IJMEI.2022.10052157
   
  
• Implementing machine learning techniques to predict bipolar disorder  
  by Nisha Agnihotri, Sanjeev Kumar Prasad 
  Abstract: Bipolar disorder (BD) is a mental and psychiatric disorder which is characterised by alternate mode swings between mania and depression is very common these days. The classification, modelling and characterisation and diagnosis of these mental disorders are important in medical research. An unexpected and unexplored area in BD is to judge the non-verbal behaviour of person accurately. Therefore, this paper address the challenges of detecting BD state by machine learning (ML) techniques to test the non-verbal behaviours activities like various facial expressions, voice recordings and body gestures of mentally ill and controlled persons in a whole spectrum. ML techniques can potentially provide new horizons in diagnosing and treating in mental healthcare. Further, this paper aims to present commonly used algorithms such as decision trees (DT), support vector mechanism (SVM), logistics regression (LR), K-nearest neighbours (KNN), etc. and describe their properties and performances which could act as a guide to select appropriate models. The study shows that people with controlled state behaves significantly different as compared to BD patients in their interpersonal accuracy (IPA). This develops a new training program to improve better understanding and psychosocial functionality in their rehabilitation.
  Keywords: machine learning; mood disorder; anxiety depression; bipolar disorder-I and II; Python; interpersonal accuracy.
  DOI: 10.1504/IJMEI.2022.10052205
   
  
• Prediction of epileptic seizure using deep learning architectures  
  by Vajravelu Ashok, J. Anitha, Isabel De La Torre Díez, D. Jude Hemanth 
  Abstract: Neurological illnesses such as epilepsy are among the most frequent. Epileptic sufferers’ lives are greatly impacted by early warnings of impending seizures. Using electroencephalogram signals, this research aims to create an epileptic seizure prediction algorithm that can automatically identify an epileptic seizure. Early seizure prediction using EEG data is now possible thanks to the latest machine learning algorithms. An average AUC of 0.74 is achieved by the new technique, compared to 0.72 for the state-of-the-art approach, a 3.25-fold increase in computing time. In-depth knowledge of seizure detection, classification, and potential future research areas may be gained through this presentation’s cutting-edge methodologies and concepts. Predicting seizure activity might benefit from a modified atom search optimisation-based deep recurrent neural network. Numerous hidden layers are used by the deep recurrent neural network (DRNN) classifier to predict seizure activity.
  Keywords: electroencephalogram; EEG; signal processing; epilepsy; CHBMIT dataset.
  DOI: 10.1504/IJMEI.2022.10052827
   
  
• Segmentation of retinal features in colour fundus images  
  by N. Bino, P.A. Haris, O. Sheeba 
  Abstract: Retinal diseases could be diagnosed by the variations in size, shape and texture of features in retinal images. Colour fundus images are widely used for the diagnosis of retinal diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), etc. Image processing enhances the input image, which segments and highlights regions of interest and quantify it. In this paper, we have segmented optic disc and blood vessels in colour fundus images using an algorithm coded in MATLAB. The input images and their corresponding ground truth images were obtained from public datasets available in Kaggle. The validation of the segmented images is done for parameters like accuracy, sensitivity, precision, F-measure, Mathews correlation coefficient, Dice coefficient, Jaccard index and specificity. The algorithm could effectively segment images, with an average accuracy of 99.74% in segmenting optic disc and 94.74% that of blood vessels.
  Keywords: retinal image segmentation; colour fundus imaging; optic disc; retinal blood vessels.
  DOI: 10.1504/IJMEI.2022.10052912
   
  
• Hybrid multi-kernel SVM algorithm for microaneurysm recognition in colour fundus images  
  by S.B. Mohan, B. Kannan, D. Ravikumar, C. Sivakumaran 
  Abstract: Diabetic retinopathy (DR) is a condition that results in impaired vision and is triggered by diabetic. These alterations in the retinal vessels may be traced back to hyperglycemia. Microaneurysms (MAs) seem to be the initial disease symptoms of DR, and a prompt identification of microaneurysms may assist in the detection of DR in the preclinical phase. Optical coherence imaging system, often known as OCT, is a non-invasive imaging technology that offers a cross-sectional picture of the retinal. We construct new probability foundations for support vector machines (SVMs) using informational differences and the Fisher score. The findings that were achieved were as follows: the accuracy criterion had a score of 96.32%, the sensitivity criterion had a score of 97.34%, the specificity criterion had a score of 95.42%, and the precision criterion had a score of 95.27%.
  Keywords: retinal pictures; scaled Dirichlet combination; support vector machines; SVMs; microaneurysm images.
  DOI: 10.1504/IJMEI.2023.10053229
   
  
• Application of deep learning to segment pelvis bones  
  by N. Thamaraikannan, M. Saravanan, N.K. Anushkannan, S. Ramesh, C. Sivakumaran 
  Abstract: The proper identification and localisation of pelvic bone metastases begins with precise segmentation of the pelvic bones. Existing pelvic bone segmentation algorithms are generally manual or semi-automatic, and they exhibit low accuracy when dealing with picture appearance changes caused by multi-site domain shifts, etc. This paper presents a strategy for segmenting normal pelvic bone characteristics in multiparametric magnetic resonance imaging (mpMRI) using a 3D U-Net based on deep learning. Extensive testing on our dataset indicates the usefulness of our automated technique. The 3D U-Net network, based on deep learning, offers patients accurate identification and segmentation of pelvic bone metastases.
  Keywords: U-Net; pelvis bone; segmentation; biomedical signal processing.
  DOI: 10.1504/IJMEI.2023.10053901
   
  
• Psychological distress and its association with socioeconomic and health behaviour factors  
  by Xiangdong An, Hui-chuan Chen, Monty Taylor 
  Abstract: The authors assess the relationships among psychological distress, socioeconomic status, and health behaviour factors. Data from the Canadian Community Health Survey (CCHS) dataset was analysed for this study using hierarchical regression and ANOVA. Results show that daily energy expenditure is positively related with psychological distress. The authors also find that daily fruit consumption, daily green salad consumption, and monthly frequency of leisure activities are negatively associated with psychological distress. Furthermore, the results indicate that people with personal incomes of less than $20,000 perceive significantly higher distress than people with higher incomes. Among five occupation groups, the sales and services group perceives significantly higher distress compared to the other four groups. The four other occupation groups as categorised by CCHS do not perceive significantly different distress levels.
  Keywords: psychological distress; gender; occupation; income; health behaviour; fruit consumption; green salad consumption; leisure activities; energy expenditure.
  DOI: 10.1504/IJMEI.2023.10053931
   
  
• A novel method to predict stroke using deep learning approach  
  by Swagata Sarkar, A. Jayashree, R. Thirumurugan, C. Sivakumaran 
  Abstract: Strokes remain the main cause of major impairment that lasts for lengthy period of time and ranks as the third greatest cause of mortality overall in United States. The ability to accurately anticipate a stroke is very useful for facilitating earlier intervention as well as treatments. Several studies have concentrated on determining the chance of a heart attack; on the other hand, only few have investigated the possibility of a brain attack. Various machine learning techniques are being created to estimate the chance of a cerebral infarction. We present a data-driven classifiers deep neural Networks (ResNet) for diagnosing strokes predicated on 12-leads ECG information. The quality of the model, which has been fine-tuned to perfection, allows us to achieve a training correctness of 99.99% and an accuracy rate of 85.82%. The findings imply that ECG is a viable adjunct tool for diagnosing stroke.
  Keywords: machine learning; stroke prediction; CNN; deep learning; ResNet.
  DOI: 10.1504/IJMEI.2023.10053942
   
  
• Face recognition-based security system for automated teller machine using deep face model  
  by K. Kavin Kumar, S. Mythili, S. Prabhu Kumar 
  Abstract: The purpose of this study is to show about using OpenCV and deep learning techniques to design and implement a face recognition-based ATM security system. Face recognition only provides service to the user if the user is authentic or if the user has been validated by an authentic ATM card user. Users are authenticated by comparing the persons video taken in front of the ATM. The proposed method identifies the right persons by comparing the blink of eyes. If someone enter the ATM with photocopy of another person, checking the blink of eyes the validation is made. If the user is authentic, to strengthen the models accuracy, the current image is being utilised. A web link is sent to the registered mobile number that owns the ATM card, to verify the access of the illegitimate user to his/her account only then the user is considered as a legitimate user. Histogram algorithms and deep learning approaches are used by the system to identify persons. To process the image and detect the faces in the image, this system use the OpenCV package. Face recognition is achieved through the use of an open CV and deep learning.
  Keywords: OpenCV; blink of eyes; VGG Face model and deep face.
  DOI: 10.1504/IJMEI.2023.10054109
   
  
• Detection and classification of lung cancer using deep neural network  
  by S. Babu Kumar, M. Vinoth Kumar 
  Abstract: Lung cancers hold a critical spot among the reasons for most cancer deaths among humans. The better way to maximise the survival rate is the detection of cancer at the earliest. But existing traditional techniques are time-consuming and error-prone. This study is a significant and efficient method for the detection and classification of lung cancer into large cell carcinomas, small cell, adenocarcinoma, squamous cell carcinomas, or benign respectively. In the proposed technique, a novel algorithm is implemented to generate the Image patches from whole slide histopathological images. Then, histogram normalisation is carried out to remove noise and enhance the image. Then a novel extended Mobius transformation technique is used for image augmentation. Finally, Dense EfficientNetB7 is trained to extract the features for the detection and classification of lung cancer. The performance of the proposed technique is proved more efficient and par with histologists attaining an accuracy of 98.87%.
  Keywords: lung cancer; histopathology; deep convolution neural network; DCNN; EfficientNetB7.
  DOI: 10.1504/IJMEI.2023.10054175
   
  
• A review of the relationship between flow-volume curve and obstructive sleep apnea  
  by Seval Bulut Eris, Cahit Bilgin, Ömer Eriş, Mehmet Recep Bozkurt 
  Abstract: This study presents a systematic review of studies in the literature evaluating the relationship between the flow-volume curve and obstructive sleep apnea (OSA). When the literature was examined, it was observed that there are few studies in this field, and only statistical analyses were made using the ready parameters given by the pulmonary function test (PFT). New biomarkers can be discovered with characteristic and statistical features extracted from the flow-volume curve to diagnose OSA. Machine learning-based systems can be developed using biomarkers. Rules-based automatic diagnosis systems can enable faster OSA diagnosis. In addition, it can eliminate the disadvantages arising from using a sleep laboratory to diagnose OSA.
  Keywords: obstructive sleep apnea; OSA; flow-volume curve; flow-volume loop; saw-tooth pattern; pulmonary function test; PFT; machine learning.
  DOI: 10.1504/IJMEI.2023.10054791
   
  
• Design and analysis of microfluidic channel with W-shaped electrodes for separation of bio-particles using dielectrophoresis technique  
  by Muktimani Brahma, R. Kumar, Trigunesh Narzary 
  Abstract: A unique microfluidic device with W-shaped electrodes is designed to separate the micro-bio-particles. The microfluidic device efficiently separates the red blood cells (RBCs) and platelets using the dielectrophoresis technique. The separation process has been analysed by using COMSOL Multiphysics software and found that the electrode with the minimum gap of 25 um acquired optimum results at an applied voltage of 8 V. The proposed device gave a high separation efficiency of 89.5% and a separation purity of 99.63%. The enhanced microfluidic device gave better results compared to other recent works and is exceptionally suitable for separation of micro-bio-particles.
  Keywords: dielectrophoresis; red blood cells; RBCs; platelets; separation; microchannel; electrodes; flow rate; electric field; non-uniform; force.
  DOI: 10.1504/IJMEI.2023.10054875
   
  
• Diffusion tensor imaging for Alzheimer’s disease classification using a bag of features and majority voting  
  by Latifa Houria, Noureddine Belkhamsa, Assia Cherfa, Yazid Cherfa 
  Abstract: Alzheimer’s disease (AD) is a neurodegenerative disease and the most common cause of dementia. Thus, various neuroimaging-based methods were proposed to detect this disease at its early stage, called mild cognitive impairment (MCI). We developed a novel approach combining Diffusion tensor imaging (DTI)-indices and ensemble learning to classify AD. A bag of features (BoF) is used to retrieve the locale features, and a support vector machine (SVM) is applied for classification. The majority voting technique is used to combine the final predicted labels. The proposed method achieves an accuracy of 94.0%, 97.0%, and 95.9% to classify CN vs. MCI, CN vs. AD, and MCI vs. AD, respectively.
  Keywords: diffusion tensor imaging; Alzheimer’s disease; bag of features; BoF; support vector machine; SVM; majority voting.
  DOI: 10.1504/IJMEI.2023.10054939
   
  
• An intelligent method for predicting cardiac disease based on PSO-convolutional neural network  
  by M. Balamurugan, P. Blessed Prince 
  Abstract: Cardiovascular disease (also known as CVD) is one of the primary contributors to both morbidity and death. The present state of the art in artificial intelligence plays a significant part in the process of aiding medical professionals in the diagnosis of a variety of disorders. A hybrid framework is proposed for the diagnosis of cardiovascular illnesses by analysing medical voice data. Eleven datasets comprising 14,416 numerical characteristics may be produced by using the method that has been suggested. From the datasets that are produced as a consequence, numerical and graphical characteristics are extracted. In the third layer, numerical data is provided to five separate machine learning (ML) techniques and graphical characteristics are transmitted to convolutional neural networks (CNNs), with transfer learning utilised to choose the best suited configurations. From a benchmark dataset, the PSO-CNN technique that was presented obtains an accuracy of 96.78%.
  Keywords: cardiovascular; PSO-CNN; optimisation; machine learning.
  DOI: 10.1504/IJMEI.2023.10054940