International Journal of Biomedical Engineering and Technology (57 papers in press)

Regular Issues

• Monitoring optical responses and physiological status of human skin in vivo with diffuse reflectance difference spectroscopy  
  by Jung Huang, Jyun-Ying Chen 
  Abstract: Fourier-transform visible-near infrared spectroscopy was applied to analyse diffuse reflectance from human skin perturbed with three skin-agitating methods. Principal component analysis (PCA) was applied to deduce three characteristic spectral responses of human skin. Based on Monte Carlo multilayer simulation, the responses can be attributed to changes in light scattering and haemoglobin and melanin content. The eigenspectra form a basis for resolving the optical responses of human skin from diffuse reflectance difference spectra measured at different time points after the skin tissue is mechanically stressed. We demonstrate that by applying this analysis scheme on in vivo measured diffuse reflectance difference spectra, valuable information about the responses of skin tissue can be deduced and thereby the physiological status of skin can be monitored.
  Keywords: diffuse reflectance spectroscopy; skin tissue; optical response; monte-carlo simulation; principal component analysis.
  
  
• A Comparative Analysis of Fall Risk Factors in Elderly and their Automatic Assessment  
  by Carolin Wuerich, Christian Wiede, Anton Grabmaier 
  Abstract: In the geriatric population, falls are a prevalent issue and can entail severe physical and psychological consequences. Fall risk assessment can provide early information in order to adopt prevention measures. However, there are many different reasons why a person might fall ranging from muscolosceletal deficits to cognitive, mental or sensory impairments, and cardiovascular diseases. While the majority of the approaches on fall risk assessment are based on gait analyses, other methods have shown that including considerations of other possible causes can significantly improve the prediction. Thus, for the development of an effective fall risk assessment and to choose the appropriate interventions, the underlying causes need to be identified. This review provides an overview of fall risk factors in the elderly population outlining the correlations between the causes, symptoms and fall risk. Moreover, the state of the art of assessment methods for the identified risk factors as well as for fall risk in general is presented.
  Keywords: aging; automation; cognitive decline; elderly; fall prevention; fall risk; fall risk factors; physiological decline; risk assessment.
  
  
• Isolation and characterization of copper resistant bacteria from khetri copper mines and analysis of the expression of copper-induced proteins  
  by Shraddha Mishra, Sanjay Kumar Verma 
  Abstract: The present study focuses on the isolation and characterization of copper-resistant bacteria from khetri copper mines and analysis of proteins expression under copper stress in selected isolate (KH-5) using SDS-PAGE analysis. A total of 14 different bacterial colonies (KH-1 to KH-14) were isolated on media containing 2 mM of copper and were further characterized for their biochemical properties. The cross-metal tolerance study exhibited their tolerance to other heavy metals (As, Zn, Ni, Co, and Cd) along with copper. The growth curve analysis of all the isolates showed a delay in the lag phase for KH-11, KH-12, KH-2, KH-3, KH-8, and KH-9 in comparison to other strains that indicate the more robust metal resistance mechanisms in other isolates. Based on the results of all these studies, KH-5 was selected for the study of protein expression in the presence of copper stress which showed the same protein band pattern as control (non-stressed condition) without induction of any new protein band in the stressed condition. This suggests the presence of a constitutive copper resistance mechanism in the KH-5. Thus, further studies can be done to explore the copper resistance mechanism in this isolate.
  Keywords: copper; tolerance; protein expression.
  
  
• New Approach for Quality Analysis of the Hearing Impaired using Combined Temporal and Spectral Processing  
  by Hemangi Shinde, Vibha Vyas, Vikram C. M. 
  Abstract: This paper proposes a novel approach of combining temporal and spectral speech enhancement methods for Hearing Impaired (HI) listeners. The temporally processed speech is combined with five different types of Maximum a-Posterior (MAP) estimators, namely, Magnitude Squared Spectrum Estimator (MSSE), MSSE using posteriori SNR uncertainty, using priori SNR uncertainty, soft masking using posterior SNR uncertainty on magnitude squared spectrum and using priori SNR uncertainty on magnitude squared spectrum. The temporal, spectral and the combined temporal spectral algorithms are evaluated in terms of quality for HI listeners using noisy speech signals at -5, 0, 5 and 10 dB SNR in a cafeteria, a station, in traffic and train noise environments. The experimental results depict that the new combined temporal spectral algorithm showed significantly better results over the individual temporal, spectral methods as well as a previous combined temporal and spectral method investigated and tested by the author earlier for HI people.
  Keywords: Speech Enhancement; hearing impaired; temporal processing; spectral processing; mean opinion score.
  
  
• An Insight into Phantom Sensation and the Application of Ultrasound Imaging to the Study of Gesture Motions for Transhumeral Prosthesis  
  by Ejay Nsugbe, Carol Phillips 
  Abstract: Transhumeral amputees account for the largest cohort of upper-limb amputees missing a substantial amount of their upper-limb, as per combined statistics with the UK and Italy. In this work, we utilise the human motor control theory, and Penfield homunculus as a basis for providing a review and school of thought behind phantom limb sensations, pain and associated therapy. Clinical work was also conducted on five non-amputated individuals using ultrasound imaging along the humerus while participants were instructed to produce a number of hand movements. This set of results has thus suggested that mainly compound gesture motions, which involve a degree of bulk muscular recruitment, can be detected along the humerus. It is foreseen that this set of gestures can be used to explore mobility and sensation of phantom limbs by clinical rehabilitation prosthetists.
  Keywords: Upper-Limb Prosthesis; Transhumeral Amputee; Ultrasound Imaging; Phantom Sensation; Homunculus; Cybernetics; Human Motor Control; Myoelectric Prosthesis; Medical Physics; Phantom Limb.
  
  
• Histopathological Image Classification using Dilated Residual Grooming Kernel Model  
  by Ramgopal Kashyap 
  Abstract: Breast cancer is one of the main reasons for death among women. Deep learning and machine learning models are contributing to the early and accurate diagnosis of Breast cancer. This research aims to contribute the medical science and technology with the novel deep learning-based model to detect the small cancer cell and the precise diagnosis of the cancer cells. The proposed model takes breast cancer Histopathological Image Classification (BreakHis) and Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD) image dataset and performs strain normalization to solve the color divergence issues. After that, data augmentation with nineteen different parameters like scaling, rotation, flip, resize, gamma value is performed to solve the overfitting issues. The proposed Dilated Residual Grooming Kernel (DRGK) model is a 19-layer model that includes proposed multiscale dilated convolution (MSDC) unit. The MSDC unit uses the dilated convolutions to extract the features very effectively, to detect small objects and thin boundary without increasing the complexity. This unit combines three small units for extractions of low-level features like edge, contour, colors, detection of small objects and to enhance the receptive field without losing the image information; it makes the computation efficient. The proposed DRGK model accelerates the process along with MSDC unit and convolution, pooling, downsampling, and dilated convolution operations. The proposed model gives better performance in terms of accuracy, average precision score, precision, sensitivity, and f1 score. Experimental results show that the proposed method outperforms many state-of-the-art ones with the accuracy of 98.50%. The total memory required by the proposed model is 32.7 M where each number takes 4 bytes, so each image takes 32.7*4MB=130.8 M.B. of memory.
  Keywords: Breast Cancer; Channel attention model; Contrast limited adaptive histogram equalization; Data augmentation; Deep learning; Dilated convolution unit; Dilated residual growing kernel model; Dilated spatial convolution; Strain normalization.
  
  
• Early Diagnosis of Alzheimer Disease using EEG Signals: The Role of Pre-processing  
  by Vinayak Bairagi, Sachin Elgandelwar 
  Abstract: Electroencephalograms (EEGs) have significant ability to measure the brain activity and have huge potential for the analysis of the brain diseases like Alzheimer disease (AD). EEG is a measurement of electrical signal generated from the neurons presents in the brain. These nonstationary EEGs signals show the sign of many current diseases or even give the warning about impending diseases. Three main effects of Alzheimer disease on EEG signal have been identified like signal slowing, reduction in EEG complexity and a change in the normal state of EEG synchrony. Brain computer interface (BCI) system gives a way for the detection of the preliminary stage of the Alzheimer disease based on nonlinear EEG signals. Pre-processing of the EEG decides the efficiency of this methodology. Artifacts must be removed before analyzing the EEG signals. Henceforth in recent year, pre-processing of EEG signals has got a great deal of enthusiasm for researchers. In this paper, state of art EEG pre-processing techniques is explored. This paper indicates clear and simple understanding of selected preprocessing techniques with respect to Alzheimer disease diagnosis.
  Keywords: Alzheimer Disease (AD); Electroencephalogram Signals (EEG); Independent Component Analysis (ICA); Filtering; Wavelet Transform.
  
  
• A Survey on Data Mining and Machine Learning Techniques for Diagnosing Hepatitis Disease  
  by Tabeen Tasneem, Mir Md. Jahangir Kabir, Shuxiang Xu, Tazeen Tasneem 
  Abstract: With the advancement of technology in recent years,rndifferent new techniques are being used for classification andrnprediction of different complex diseases, as well as to analyzernbiomedical data in the medical field. Hepatitis is a liver diseasernthat has an adverse influence on people of any age group andrngenerally no symptoms appear. Hence, the diagnosis of hepatitisrnin the early stage becomes crucial. Use of technology can easernthe process and so researchers have proposed some classificationrntechniques for early detection of hepatitis. This paper aimsrnat summarizing the up-to-the-minute techniques used for therndiagnosis and prediction of hepatitis and in order to fulfill therngoal, numerous articles from 1996 to 2020 have been investigated.rnThis research work can be helpful to develop new techniques inrnfuture by knowing the pitfalls of the previous ones.
  Keywords: Hepatitis diagnosis; Data mining; Machine learning; Classification; Disease prediction.
  
  
• Numerical Analysis of Artificial Hip Joints: Effect of Geometry  
  by Abhishek Kumar Singh, Abhishek Mishra 
  Abstract: The present work deals with the comparison analysis of solid and hollow hip joint implant. A three-dimensional finite element model of hip joint implant is developed using ANSYS 18.0 for determination of contact stresses, sliding distance and deformation caused due to loading on the joint in the standing condition. The finite element contact stresses generated on the contact surfaces of hip implant model along with the sliding distance has been used in for FEM analysis. Result of analysis shows that total deformation in the joint for smaller femoral head diameter is less for the hollow femoral head than solid femoral head, but as the size of the femoral head and other components are increased, total deformation in the hollow femoral head comes out as more than that of solid femoral head.
  Keywords: Artificial hip-joints; solid femoral head; hollow femoral head; FEM analysis.
  
  
• An Ultrasonic sensor driven obstacle detection and localization system in 3D space for Visually Impaired Persons  
  by Bhupendra Singh 
  Abstract: There are several challenges faced by Visual Impaired persons while travelling through the outdoor environment. The white canernmost commonly used by them for obstacle detection in their route hasrnits limitation with the inability to detect obstacles above waist height.rnDue to this limitation head injury is very commonly faced by the VisuallyrnImpaired persons. In this work, we have developed eyeglasses whichrnconsist of two Ultrasonic sensors and two buzzers for obstacle detectionrnand localization. The location of the obstacle in 3D space is conveyedrnto the user with varying frequency patterns through the buzzers. The 3Drnlocation of the obstacle is conveyed in terms of laterality, elevation andrndepth information. Upon testing the system for the effectiveness in detecting the obstacle in 3D space, it is found as 70.5% laterality detectionrnrate, 70.5% elevation detection rate and 80.8% depth detection rate. Onrncomparing our results with similar results reported in the literature as arnstate of the art, our results outperform them all.
  Keywords: Assistive Technology,; Electronic Travel Aids; ;Healthcare,;rnSensors; ;Visual Impairment.
  DOI: 10.1504/IJBET.2021.10049267
   
  
• Simulation of insufflation gas via an alternative Multi-functional Forceps with applications in Laparoscopic Surgeries  
  by Md. Abdul Raheem Junaidi, Harsha Sista, Daseswara Rao Yenduluri, Ram Chandra Murthy K 
  Abstract: Purpose: To simulate the gas flow in a multi-functional laparoscopic instrument using ANSYS FLUENT software. \r\nMaterial and Methods:The laparoscopic procedure used by surgeons is a minimally invasive surgery to operate upon the abdominal cavity. The Suction-Irrigation (S-I) process is used to clean and disinfect the abdominal cavity to enable safe and efficient surgical intervention. In most surgeries, the dissector forceps are repeatedly exchanged with the S-I device to operate and clean the surgery site. The improved forceps is a combination of a suction-irrigator and a dissector forceps. \r\nResults:A more comprehensive CFD flow analysis of the improved forceps, the flow of CO2, is simulated in the present work for different driving pressures. The resulting flow rate of CO2 is compared among the prospective designs and the S-I device currently used. The results are investigated with the help of contours plots. \r\nConclusion:The new surgical forceps eliminates re-insertion of dissector with suction-irrigator and is reusable, multi-functional, non-toxic, corrosion-resistant, toughened, and cost-effective. In addition, this forceps aids in reducing the time of surgery, fatigue to the surgeon, and trauma to the patient. This can also potentially benefit in single port and robotic laparoscopic surgeries.\r\n
  Keywords: Computational Fluid Dynamics; Forceps; Newtonian; S-I device; Insufflator; multi-functional instrument.
  DOI: 10.1504/IJBET.2021.10048204
   
  
• Development of non-contact optical device for monitoring neonatal jaundice based on the skin color of the upper trunk using skin reflectometry  
  by Vignesh Kumar Kanamail, Periyasamy R, Senthil Kumar K, Suresh Chelliya D, Senguttuvan D 
  Abstract: Jaundice occurs in new born babies within few days of birth due to elevated bilirubin levels in the blood and also the most common causes of hospital admission of young infants. In general, skin colour changes in new born are visually assessed and total serum bilirubin (TSB) level are measured through blood sampling method for identifying the severity of jaundice. Transcutaneous Bilirubin (TcB) is often preferred as an alternative method to avoid frequent blood sampling. However, this method has a challenge in dealing with neonates in countries of the Indian subcontinent where babies have distinctive skin colour. Hence the aim of this paper was to develop a non-invasive, non-contact handheld optical device (460nm LED light source and a photodiode) to measure bilirubin concentration in neonates of Indian subcontinent based on the skin reflectance. The device was tested with mock bilirubin samples (n=8), human blood serum samples (n=8) and on neonates in Neonatal Intensive Care Unit (n=39). The results were validated with TSB value and positive correlation factor of R =0.95 to 0.99 was observed between TcB and TSB by applying first order linear regression analysis. Therefore, the proposed indigenously developed device was successfully detected the jaundice by estimating the bilirubin concentration in neonates based on skin reflectance.
  Keywords: Neonatal Jaundice; Non-Invasive Bilirubin Monitoring; Optical method; Skin Reflectance; Transcutaneous Bilirubin.
  
  
• Implementation of machine learning algorithms for automated human gait activity recognition using sEMG signals  
  by Ankit Vijayvargiya, Balan Dhanka, Vishu Gupta, Rajesh Kumar 
  Abstract: Recognition of various human gait activities based on the sEMG signal has an important role to control the exoskeleton or prosthesis. These robotic assistive devices are used for enhancing the physical performance of an injured or disabled person. In this paper, a comparative assessment of various computational classifiers is presented for the recognition of different gait activities from the sEMG signal. Analysis of sEMG signal is complicated because of a multiple muscle contribute to a single activity and the effect of other muscles produces noise. So, first, we have applied the discrete wavelet transform to the sEMG signal based on the Daubechies wavelet and then extracted eleven-time domain features. Thereafter, features are standardized and fed to eight different computational classifiers. The performance indices of classifiers are calculated for ten runs. The results suggest that the MLP Classifier gives the highest accuracy (97.72%) in identifying different gait activities from sEMG signals.
  Keywords: Human Gait Activity Recognition; Discrete Wavelet Transform (DWT); Computational Classifier; Surface Electromyography (sEMG) Signal.
  
  
• Differences in Kinematic Variables in Single Leg Stance test between young and elderly people  
  by David Perez Cruzado, Manuel Gonzalez Sanchez, Antonio Cuesta Vargas 
  Abstract: Background. Parameterising the Single Leg Stance test could be useful in clinical practice and basic research. The aim of the present study was to understand the intergroup and intragroup differences in kinematic variables among young adults and older adults in the performing of Single Leg Stance test. Methods. Two groups of participants were measured, 6 individuals over 65 years old and 6 individuals between 20-25 years old. Inertial sensors were located in the trunk and in the lumbar zone. Results. Significant differences between groups were found in the lumbar and trunk sensor in different movements (flexo/extension, inclination and rotation). Significant differences between the dominant and non-dominant leg were not found. Conclusion. There were significant differences between both groups. It is also important to highlight the excellent values of reliability of the inertial sensors.
  Keywords: elderly; aging; kinematics; balance; inertial sensor.
  
  
• PPG based Windkessel Model Parameter Identification via Unscented Kalman Filtering  
  by Akhil Walia, Amit Kaul 
  Abstract: Modeling of arterial system is helpful in understanding the cardiovascularrnsystem and related ailments. Among various methods, Windkessel model is one approach which plays signicant role in understanding the working principle of natural arterial system. The windkessel models describe the hydraulic properties of arterial system. In this paper, PPG based windkessel model has been suggested which utilizes PPG signal as measurement. State dynamics of proposed model has also been developed. The main contribution of this work lies on the identication of model parameters using Extended Kalman lter (EKF) and Unscented Kalman lter (UKF). Estimated parameters are compared with nominal values to validate model structures. The comparative analysisrnhas been carried out with the pre-existing method. Execution time taken to simulate the proposed model for modeling a single PPG pulse is approximately one second.
  Keywords: Windkessel model; Compliance; Inertance; Unscented Kalman Filter (UKF).
  
  
• A novel hybrid system for detecting epileptic seizure in neonate and adult patients  
  by Ahmed Adda, Hadjira Benoudnine, Mohamed Daoud, Philippe Ravier 
  Abstract: Epilepsy is a brain disease characterized by recurrent seizures. Electroencephalography (EEG) is a prominent tool used in clinical routine for monitoring and diagnosing seizures. Visual inspection of EEG traces is a time-consuming and laborious process. The literature survey shows that though some advanced methods suggested for automatic seizure detection perform quite well in case of adult patients, they fail in discovering neonatal seizure activity, due to the fact that neonatal seizures are less prominent than adult seizures. Therefore, this research proposes a generalized automatic system for detecting seizures in epileptic patients regardless their ages. The proposed system takes advantage of hybridation between generalized Hurst exponent (GHE) and approximate entropy (ApEn) features extracted from the amplitude envelope of EEG signals. These features are taken as input parameters of the support vector machine (SVM) classifier, which distinguishes EEG signals based on the existence or not of seizures. In order to assess the generality of the proposed technique, binary test (normal vs. seizure) was achieved on two independent datasets, including Bonn University EEG database for adults and that of neonatal EEG collected at the Royal Womens Hospital, Brisbane, Australia. In the first dataset, our system detects seizures with an accuracy of 99 %, whereas in the second dataset, the proposed system reached an accuracy of 100%. The experimental results show that the proposed method demonstrates superiority to existing systems by solving the seizure detection tasks with a single automatic system, which shows very high accuracy for both neonate and adult patients. Such a system could help neurologists in the visual analysis, diagnosis of long-term EEG recordings and considerably reduces the time required for this process.
  Keywords: Electroencephalogram; epilepsy; seizure; Signal envelope; Hurst parameter; Entropy.
  
  
• Fetal Brain Extraction using Mathematically Modelled Local Fetal Minima  
  by Durgadevi Paramasivam 
  Abstract: Division of the cerebrum from fetal MRI is a generally new field, with little work distributed on completely programmed preparation. Programmed mind division strategies produced for MRI of fetal brain images can\'t be straightforwardly applied to consider the creating fetal cerebrum in utero, since the fetal mind is altogether extraordinary regarding math just as tissue morphology. In this paper, the proposed segmentation techniques, to separate brain parcel from the MRI of the human embryo and in forthcomings days decided to determine the abnormality of the fetal brain at various gestational weeks. Lately, an assortment of division techniques has been proposed for the programmed depiction of the fetal and neonatal cerebrum MRI. These strategies mean to characterize areas of the premium of various granularities: mind, tissue types, or more limited constructions. Various philosophies have been applied for this division task and can be grouped into the solo, parametric, characterization, atlas combination, and deformable models. Cerebrum atlases are usually used as preparing information in the division interaction. Difficulties identifying with the picture securing, the quick mental health just as the restricted accessibility of imaging information anyway thwart this division task. This paper discusses fetal brain segmentation using mathematically modelled fetal brain minima by using a curve fitting segmentation technique. Broad tests show that the proposed approach beats the ebb and flow techniques explicitly Watershed extraction, Otsus extraction, Edge detection based extraction, and Histogram based extraction. The results dictated by applying the proposed calculation and results gained are significant
  Keywords: Fetal MRI; Brain Localisation; fetal minima; automatic; curve fitting; smoothing filter; thresholding; segmentation; Structural Similarity Index (SSIM). .
  
  
• Depression Diagnosis Using a Hybrid Residual Neural Network  
  by Mahsa Ofoghi Rezaei, Somayeh Makouei, Sebelan Danishvar 
  Abstract: Depression is one of the most widespread psychiatric disorders. EEG signals can be utilized as a tool to diagnose depression objectively. This paper employs a hybrid method to classify healthy and depressed signals, which uses a pre-trained ResNet101 to extract features automatically. Thereby, the problem of designing and training deep networks for automatic feature extraction is solved. The hypothesis in the present study is that feature-extraction layers in ResNet101 also perform desirably in detecting depressed signals. In hybrid structures, SVM, KNN, and DT classifiers are used for final classification purposes. ResNet101-SVM, ResNet101-KNN, and ResNet101-DT structures have reached accuracy of 93.8%, 90.1%, and 82.1%, respectively. Moreover, for the ResNet101-SVM structure, which has shown the best performance among all structures, the accuracy, sensitivity, and specificity are 94.7%, 94.0%, and 95.2% after applying the 10-fold cross-validation method. The results indicate the proper performance of all structures, especially the ResNet101-SVM structure, in diagnosing depression.
  Keywords: Depression; Diagnosis; Classification; EEG; Deep learning; Residual network; Hybrid model; SVM; KNN; DT.
  
  
• Design of Artificial Pancreas (AP) based on HGAPSO-FOPID Control Algorithm  
  by Akshaya Kumar Patra, Anuja Nanda 
  Abstract: This manuscript presents the design of Hybrid Genetic Algorithm-Particle Swarm Optimization-Fractional Order Proportional Integral Derivative (HGAPSO-FOPID) controller to inject the optimal dose of insulin through the AP for Blood Glucose (BG) regulation in Type-I Diabetes Mellitus (TIDM) patients. In this strategy, the controller parameters are tuned based on the Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) technique for better control execution. The productivity of the HGAPSO-FOPID controller as to accuracy, robustness and stability is tested by use of MATLAB and SIMULINK. The procured outputs reveal the better implementation of HGAPSO-FOPID controller to regulate the BG level within the range of normo-glycaemia (70 120mg/dl). The justification of improved control execution of the HGAPSO-FOPID controller is revealed by the relative result examination with other prominent control techniques.
  Keywords: BG level; AP; MID; HGAPSO-FOPID controller; diabetes.
  
  
• Artificial Intelligence Methods for Image Classification Applied to Biological Sounds for the Early Diagnosis of Cardiorespiratory Pathologies and COVID-19 Infection  
  by Agostino Giorgio 
  Abstract: With the spread of the COVID-19 pandemic, the scientific community took prompt action to seek adequate solutions for the prevention and treatment of the disease. However, what seems less developed at present are methods for early diagnosis of the disease which would be useful especially when it is becoming more complicated towards interstitial pneumonia which is the main cause of ICU admissions and deaths. The aim of this work is to describe methods typically used for signal and image digital processing, especially artificial intelligence (AI) algorithms, which could allow a very early diagnosis of the onset of COVID-19 infection as well as many other respiratory and cardiac pathologies. For this purpose, at least for a first screening, the use of medium-capacity smartphones may also be sufficient, without the need to resort to expensive medical equipment and diagnostic tests that require long waiting times and are always onerous.
  Keywords: COVID-19; Artificial Intelligence; Digital Signal Processing; Matlab; Digital Medical Devices; Biological sounds; Auscultation.
  
  
• Regression transfer learning for the prediction of three-dimensional ground reaction forces and joint moments during gait  
  by Goksu Avdan, Sinan Onal, Banafsheh Rekabdar 
  Abstract: Clinical gait analysis is a useful tool for assessing a patients walking conditions. Force platforms are gait analysis tools used to collect the ground reaction forces (GRFs); however, they are expensive and time-consuming. Therefore, this study focuses on the prediction of GRFs and joint moments without using force platforms. To address this problem, we proposed to combine deep learning methods with regression transfer learning (RTL). The inputs of the proposed method are joint angles and marker trajectories from a public dataset. Principal component analysis (PCA) has been used to reduce the data dimensionality to improve the computational time and prediction accuracy. A synthetic dataset has been generated to pre-train the deep learning method for transfer learning purpose. The experimental results indicate that the proposed transfer learning method increases the target domains learning process and can successfully predict the average GRFs and joint moments with 97.44% and 96.56% accuracy, respectively.
  Keywords: Regression transfer learning; Deep learning; 1D convolution neural network (1D CNN; Gait analysis; Ground reaction forces.
  
  
• Diagnosing Cardiovascular Diseases from Photoplethysmograph: A Review  
  by Devaki V, Jayanthi Thiruvengadam 
  Abstract: The peripheral blood volume are the variations measured at the skin surface, using a source of light and a photo detector, which is performed by a non-invasive technology called Photoplethysmograph. At recent times, much interest has been shown by countless researchers worldwide to obtain valuable information from pulse waveform besides oxygen saturation measurement, pulse rate and heart rate evaluation. Photoplethysmograph based techniques are most preferable method for the wearable devices. Each derivative of pulse waveform carry beneficial information related to health. The evolution in the wearable cardiac monitoring devices has paved a way for the individualized medical care. The application of multi-spectral photoplethysmography in various areas of cardiovascular diseases such as myocardial infarction, arterial parameters estimation, arrhythmias, Heart Rate Variability detection (HRV) discussed by researchers are presented in this article. This review gives an extensive overview on currently improved technologies that are applied in cardiovascular disease diagnosis using multi-spectral photoplethysmograph
  Keywords: Cardio-vascular diseases; Non-invasive diagnosis; Photoplethysmography; Wearable devices; optical method; multi wavelength.
  
  
• MASTER SLAVE CONFIGURATION IN ROBOTIC SURGERY THROUGH IMAGE PROCESSING  
  by Seema , Jasbir Singh Saini, Sanjeev Kumar 
  Abstract: This paper introduces the implementation of a master slave configuration set up for robotic surgery. Image processing has been used for establishing the same. A hardware set up has been designed which interfaces with a GUI. The two work in conjunction to achieve the controlled movement of a robotic arm. The software section is divided into two sections basically, Image Dataset Selection and Validation and the development of a suitable GUI. The image dataset selected was a set of 140 MRI brain tumour related images. The designed GUI allows the surgeon to capture the patient live view and set an image for processing and using further. Also, the hardware setup is controlled from the master/ doctors end. This selected frame goes through the process of image segmentation method that had been selected and improved.
  Keywords: Haptics; master slave configuration; image processing.
  
  
• Reduction in wear loss of Ultra-high molecular weight polyethylene composite under Human body temperature  
  by RAVIVARDHAN N A, Jagadish T 
  Abstract: Ultra-high molecular weight polyethylene (UHMWPE), a standard material used in artificial joints, generates wear debris when used in total hip replacement. In this study UHMWPE polymer reinforced with Multi-walled Carbon Nanotubes (MWCNT) has been developed to enhance the wear resistance property of the material. The compressed composite specimens were subjected to wear test at different load under room temperature and at 40C, i.e. close to human body temperature. This is the first report on the effect of human body temperature on wear properties of composite materials. Wear properties were studied for UHMWPE and UHMWPE-MWCNT composites. The study has confirmed a reduction in wear loss of UHMWPE by reinforcement with MWCNT and a high-level reduction in wear loss of the composite materials on exposure to human body temperature. The findings of this study have made a great contribution to joint transplantation therapy by providing valuable input regarding wear-resistant implant material.
  Keywords: Biomaterials; Composites; UHMWPE; MWCNT; wear loss.
  
  
• Three Dimensional Reconstruction Of Brain Tumors From 2D MRI Scans: Optimized Curve Fitting Process  
  by Sushitha Susan Joseph, Aju Dennisan 
  Abstract: This paper intends to introduce a 3D reconstruction model along with the solution of curve fitting problem via optimization process. This helps the model to sustain the accurate construction by estimating the boundary, corner points etc. To make the better adjustment the parameters in the curve fitting process are optimized by a new Clan Updated Grey Wolf Algorithm (CUGWA), which is the hybrid version of conventional GWO and EHO algorithms. The boundary fitting is precisely done by considering the minimization of RMSE among original and fitted boundaries. Finally, performance of the adopted method is validated over other existing schemes with respect to curve fit analysis and convergence analysis.
  Keywords: 3D Reconstruction; Brain Tumor image; Parameterization; Bezier curve; Optimization.
  
  
• An Investigation of Retinal Descriptors on Indian database for Automatic Pathological Diagnosis and Classification of Retinopathy of Prematurity  
  by Sushma Kadge, Sanjay Nalabalwar, Anil Nandgaonkar, Parag Shah, V. Narendran 
  Abstract: Early diagnosis is crucial to prevent blindness in preterm neonates. Scarcity of specialists indicates an urgent need for automated identification, classification and diagnosis of Retinopathy of Prematurity (ROP). Previous automation works didn't consider the classification of ROP stages which is an essential decision maker in treatment. We developed an automatic assessment system for ROP classification (AASRC) on Indian databases. We studied stochastic gradient descent (SGD) along with five other classifiers. 64 experiments were conducted to explore the intra database based on Gray-Level Co-Occurrence Matrix (GLCM) descriptors using various frequency based parameters for classification of ROP. Information gain (IG) scoring function is used to identify best descriptor while Students t-tests is used for validation. The classification accuracy rate of ROP disorder achieved are 99.03%, 93.87%, 94.55%, 92.51% and 97.95% respectively for Normal/Abnormal, Stage 1, Stage 2, Stage 3, and Stage 4. The experimental findings demonstrate the proposed feature descriptors and classifier outperforms state-of-the-art models.
  Keywords: Retinopathy of Prematurity; ROP Retina Image analysis; Classification; Stochastic gradient descent and GLCM.
  
  
• DEEP LEARNING BASED GENDER CLASSIFICATION WITH DENTAL X-RAY IMAGES  
  by Vijayakumari B, Vidhya S, Saraya J 
  Abstract: The long-lasting part in human body is teeth and even after the death of the person it remains un-affected. Hence in Forensic department, teeth play a crucial role to recognize a dead or missing person. In Forensic analysis, gender difference is a considerable course of action. Yet, gender identification with dental images using deep learning methods are still in research. An algorithm is proposed in this paper to find human gender using panoramic Dental X-ray Images (DXI). This work is organized as three sections such as Image Pre-processing, Gradient Based Recursive Threshold (GBRT) segmentation and classification. Initially, using prime magic square filter the unwanted noises are removed. Secondly, to perform segmentation GBRT is used. Finally with Resnet50 network, the gender is classified. The dataset of 285 dental images were taken and they are augmented to 4000 dental images and then they are separated as 3000 images for training and 1000
  Keywords: Gender classification; Dental radiographs; Morphological operations; GBRT segmentation; Deep CNN ResNet50 classified results.
  DOI: 10.1504/IJBET.2023.10050046
   
  
• Diagnosis Results of Athletes with Ankle Joint Pain Based on the Neutrosophic Ensemble Image  
  by Guoqing Shi 
  Abstract: This article is mainly to study the diagnosis results of athletes with ankle joint pain based on the neutrosophic set of images. In the experiment of this research, the eutrophic ensemble image technology was used to diagnose and analyse the patients. At the same time, the PACS system in the construction method of medical image diagnosis knowledge base is used to realise medical image information management, integrate medical images, and improve the utilisation rate to diagnose patients with ankle joint pain. Through reconstructed image quality and EBCOT coding technology, the accuracy of medical images is improved. Compared with routine examination, the effect of medical imaging examination is much better. The diagnosis rate of patients is much higher than that of routine examinations, which also improves the satisfaction and trust of patients.
  Keywords: sports injuries; ankle joint pain; neutrosophic image; neutrosophic imaging; imaging diagnosis.
  DOI: 10.1504/IJBET.2023.10050485
   
  
• Unveiling the potential of complex network in coronavirus proliferation study  
  by S. Sankararaman 
  Abstract: The development of novel methods for understanding virus replication is the need of the time of the COVID-19 pandemic. The present work proposes a novel surrogate graph-based method for understanding SARS-CoV-2 replication. Constructing a time history pattern (THP) matrix from the video of the virus interaction with normal cells, the inertia moment (IM) and complex network features are determined. The variation of IM and the graph features are correlated with the proliferation of SARS-CoV-2. Thus the work suggests the possibility of complex network and IM analyses to understand the kinetics of the virus infection.
  Keywords: graph theory; coronavirus; proliferation; inertia moment; complex network.
  DOI: 10.1504/IJBET.2022.10051583
   
  
• CT and MRI Image Fusion Via Dual-Branch GAN  
  by Wenzhe Zhai, Wenhao Song, Jinyong Chen, Guisheng Zhang, Qilei Li, Mingliang Gao 
  Abstract: CT and MRI image fusion is a popular research field that plays a vital role in clinical diagnosis. To retain more salient features and complementary information from source images, we propose a dual-branch generative adversarial network (DBGAN) to fuse the CT and MRI images. The proposed DBGAN is designed in a dual branching structure schema, which consists of a couple of generators and discriminators. The generators and discriminators establish a generative adversarial relationship so that the fused images generated by the generators are indistinguishable from the discriminators. Furthermore, we employ the multiscale extraction module (MEM) and self-attention module (SAM) in the generators to enhance the salient features and detailed information of the fused images. The subjective and objective evaluation demonstrate the superiority of the proposed method over the state-of-the-art methods.
  Keywords: image fusion; generative adversarial network; CT/MRI image; healthcare.
  DOI: 10.1504/IJBET.2023.10051630
   
  
• Retina blood vessels segmentation by combining deep learning networks  
  by Bachiri Mohamed Elssaleh, Adel Rahmoune, Faycal Rahmoune 
  Abstract: In this paper, we propose two deep learning architectures for the segmentation and detection of the vascular networks of blood vessels in fundus images. First, we combined VGG16 with U-net, then, we used Resnet 34 in combination with U-net. Both architectures employ an encoding and a decoding path. In this paper, we used the DRIVE and STARE databases. After applying VGG 16+U-net on the DRIVE database, we obtained the accuracy value of 0.96955, 0.79929 sensitivity, 0.98624 specificity, 0.9805 recall, and 0.9833 F1-Score. We applied VGG 16+U-net on STARE database and we got 0.95259 accuracy, 0.89996 sensitivity, 0.95530 specificity, 0.9933 recall, and 0.9742 F1-Score. Concerning Resnet 34 + U-net, we got the value of 0.9692 accuracy, 0.7859 sensitivity, 0.9870 specificity, 0.9794 recall, and 0.9832 F1-Score after applying on DRIVE database. Moreover, we got 0.9363 accuracy, 0.9335 sensitivity, 0.9246 specificity, 0.9961 recall, and 0.9649 F1-Score after we applied Resnet 34+U-net on STARE.
  Keywords: retinal segmentation; convolution neuron network; U-Net; deep learning; VGG 16; Resnet 34.
  DOI: 10.1504/IJBET.2022.10051639
   
  
• Development of a mathematical correlation for polydisperse non-spherical drug particle deposition in the human upper respiratory system  
  by Sanaz Aghaei, Hassan Khaleghi 
  Abstract: Estimating the drug particle deposition in the upper respiratory system is essential to provide more effective treatment for respiratory diseases. This study numerically investigates the effect of both particle size distribution and particle shape on the total deposition efficiency in the human upper respiratory system. To investigate the effect of particle size distribution, spherical monodisperse and polydisperse particles are compared. Non-spherical polydisperse particles are also studied to investigate the effect of sphericity. It is concluded that by decreasing particle size and increasing particle sphericity, the total deposition efficiency decreases. This means that more particles escape from the upper airways to the bronchi and bronchioles. Therefore, for lung disease treatment, finer particles with higher sphericity are more suitable. Furthermore, a mathematical correlation is developed to represent the total deposition efficiency as a function of Stokes number and sphericity. This correlation estimates the deposition of both spherical and non-spherical polydisperse particles.
  Keywords: polydisperse particles; non-spherical particles; total deposition efficiency; mathematical correlation; idealised upper respiratory model.
  DOI: 10.1504/IJBET.2022.10051642
   
  
• CT image super-resolution reconstruction via Pixel-Attention Feedback Network  
  by Jianrun Shang, Guisheng Zhang, Wenhao Song, Mingliang Gao, Qilei Li, Jinfeng Pan 
  Abstract: Computed tomography (CT) imaging has been widely used in clinical medicine, and high-resolution CT images play a crucial role in the determination of lesions. To fully excavate the contributive information of initial features and improve the feature representation ability of the model, we propose a pixel-attention feedback network (PAFNet) for CT image super-resolution reconstruction. Specifically, the PAFNet adopts multi-feedback network as backbone to make full use of initial features. Subsequently, a gated feedback (GF) block is introduced to refine the underlying features using the feedback features. To enrich the output characteristics and pay attention to essential details, a pixel attention mechanism is adopted to the self-calibration convolution. The subjective and objective evaluation demonstrate the superiority of the proposed method over the state-of-the-art approaches.
  Keywords: super-resolution; CT image; pixel attention; feedback network.
  DOI: 10.1504/IJBET.2023.10051832
   
  
• Research and Design of Online Drug Mall System Based on SOA  
  by Yong Peng, Shi Wang 
  Abstract: The development of online drug mall system is getting faster and faster. However, when the online drug mall system expands various subsystems with different functions to meet various drug sales rules, the information interaction between each subsystem and the management of different systems have become a problem to be solved urgently. In view of this phenomenon, the traditional online drug mall system needs a unified integrated platform with simple structure and convenient management. We propose and develop an online drug mall system based on the SOA architecture, the SOA architecture can effectively improve concurrency, scalability, flexibility and low maintenance cost of the online drug mall system. The system fully implements the required functions, and has certain stability and reliability, and can provide users with a good user experience.
  Keywords: service oriented architecture; online pharmacy; data interaction; web service; distributed services.
  DOI: 10.1504/IJBET.2023.10052123
   
  
• Asymmetry in People with Transtibial and Transfemoral Amputation for the Activities of Daily Living  
  by Mohammad Shah Faizan, Swati Pal 
  Abstract: Asymmetry between the prosthetic and the intact leg may cause discomfort and seriously deteriorate people’s quality of life. It is important to know the current status of asymmetry involved in the recent leg prosthetics so that efforts will be made to minimise it. In this paper, 31 articles that focus on the asymmetry in people with unilateral transtibial and transfemoral amputation were screened using PRISMA. The articles were further reviewed and computed for the symmetry index. The results revealed the presence of a high level of asymmetry during various activities performed. The level of asymmetry decreases with the activities associated with increasing speed. The microprocessor-controlled prosthetics have lesser asymmetry as compared to the non-microprocessor-controlled. The recent prosthetics were not fully effective in minimising the asymmetry, thus, more advanced research is needed in the design of prosthetics, by taking into consideration the varied nature of daily activities.
  Keywords: leg prosthetics; microprocessor-controlled prosthetics; non-microprocessor-controlled prosthetics; unilateral amputation; asymmetry; symmetry index; activities of daily living; biomedical devices.
  DOI: 10.1504/IJBET.2022.10052342
   
  
• Automated Hard Exudate Segmentation using Neural Encoders and Attention Mechanisms for Diabetic Retinopathy Diagnosis  
  by Pratiksha Gawas, Sowmya Kamath S. 
  Abstract: Diabetic retinopathy (DR) is a complication caused by increased blood glucose levels, which causes retinal damage in diabetic patients’ eyes. If not discovered and treated early, it can lead to vision loss. Hard exudates (HE) is one its characteristic signs. Identification of HE is a paramount step in early diagnosis of DR. In this work, the suitability of U-Net-based deep CNN with different encoder configurations and attention gates (AG) is experimented, for HE segmentation. The proposed models were benchmarked on the standard IDRiD dataset. To overcome the challenges related to the limited dataset, data augmentation techniques were also applied to generate image patches and used for model training. Extensive experiments on the dataset revealed that U-Net with AG achieved an accuracy of 98.8%. The U-Net with ResNet50 as the encoder backbone achieved an accuracy of 98.64%. The findings show that the presented models are effective and suitable for early-stage clinical diagnosis.
  Keywords: hard exudate; hard exudate segmentation; neural encoders; attention mechanism; diabetic retinopathy; diabetic retinopathy prediction; medical informatics; deep learning.
  DOI: 10.1504/IJBET.2022.10052447
   
  
• Exploration of Functional Connectivity of Brain to assess Cognitive and Physical Health Parameters using Brain-Computer Interface  
  by Murugavalli K, Ramalakshmi R. Ramar, Pallikonda Rajasekaran Murugan, Vaibhav Gandhi 
  Abstract: The neural brain activations are triggered or stimulated by predetermined external influences, including music, videos, audio, meditation and several others. The impact of diverse stimuli on the brain is the core investigation purpose of this research. This paper evaluates the response of the participants in different frequency bands, and also in the various brain regions, to better understand the impact. Sixty five peer-reviewed publications were examined depending on the stimuli: yoga and meditation, music, taste, scent, emotion, imagery and movement. Comprehensive research was undertaken to describe stimuli and their effects on brain functional connectivity. The importance and effect of the infinity walk on changes in humans’ cognitive and physical health parameters, as well as on mental health, is also investigated and perhaps to identify the active brain region in people who have practised the infinity walk. This technique assists in the identification and justification of the truth behind the infinity walk.
  Keywords: brain-computer interface; BCI; electroencephalography; EEG; functional connectivity; FC; infinity walk; figure-of-eight walk.
  DOI: 10.1504/IJBET.2022.10052922
   
  
• A Comprehensive Review on MRI to CT and MRI to PET Image Synthesis Using Deep learning.  
  by Meharban M. S, Sabu M. K, T. Santhanakrishnan 
  Abstract: Image synthesis is the process of generating a synthetic image with desired qualities. Although CT and PET images are suffering from ionising radiation, MRI images are free from such radiation. Due to this fact, we need a system to generate synthetic CT and PET images from MRI images. The system will be helpful to avoid such ionising radiation from CT and PET and makes a better patient treatment workflow. This work reviewed various deep learning synthetic CT and synthetic PET generation methods. More than 75 papers were selected from PubMed and ScienceDirect databases from 2017 to 2021. Recently CycleGAN variants produce better results and no need for paired data. However, an effective evaluation measure was not available to evaluate the efficacy of the proposed works. More blind tests involving radiologists are required to evaluate the visual quality of the synthesised image.
  Keywords: computed tomographic; CT; positron emission tomography; PET; magnetic resonance imaging; MRI; generative adversarial networks; GAN.
  DOI: 10.1504/IJBET.2022.10052929
   
  
• RF-INDUCED HEATING EFFECTS OF METALLIC BIOMATERIAL DESIGNS AT 1.5T MRI EXPOSURE  
  by Gulsen AKDOGAN, O. Burak ISTANBULLU 
  Abstract: This study analyses the performance of implantable bio-metals in Magnetic Resonance Imaging (MRI) conditions to ensure that patients with MRI-conditional implants are not precluded from MRI applications. 316L, 316LVM, Ti-alloy, and CoCrMo-alloy specimens in different geometries were placed in a phantom that imitates the thermal/electrical features of human anatomy. The phantom was scanned in a 1.5T-MRI using Axial-T1-Gradient-Echo, Sagittal-T1-Gradient-Echo, Axial-T2-Spin-Echo and Sagittal-T2-Spin-Echo imaging sequences. The specimens were examined regarding Radiofrequency (RF) induced heating using a 2-channel-fiber-optical-temperature-transmitter and magnetic deflection/torque formation. 316LVM specimens that are not allowed to be in the MRI environment were found to be acceptable in the 1.5T-MRI environment since there was no magnetic deflection and RF-induced overheating. Ti-alloy specimens were found harmless under the same conditions. 316L and CoCrMo-alloy specimens were considered hazardous due to the magnetic deflection formation. This study demonstrates that bio-metals which were not allowed to be in MRI devices such as 316LVM are not harmful to patients in 1.5T MRI.
  Keywords: Biomaterials; Material Design and Analysis; RF-Induced Heating; MRI; Thermal Properties; Material Characterization; Safety and Hazards.
  
  
• BLDA-CSWDT Autoimmune Thyroid Disease Risks Predictive Model using Machine Learning and Deep Feature Extraction Techniques  
  by Nagavali Saka, S.Murali Krishna 
  Abstract: Nowadays, different thyroid disorders are observed which are affecting the human population worldwide. Hence, to provide suitable treatment and be cost-consuming for the patients, an earlier diagnosis is required. To improve prediction, this paper proposed Bayes-linear discriminant analysis (B-LDA) and cuckoo search based weighted decision tree (CSWDT) models to predict the autoimmune thyroid risk assessment from the obtained dataset. Initially, after pre-processing, the features are extracted using the deep MLP model, and the significant features are fused by using the B-LDA model which overcomes the dimensionality reduction issue. Further, the classification is performed by using the optimised cuckoo search with a weighted decision tree model. In addition, K-fold cross-validation is performed and attains a better accuracy value of 99.5% in thyroid disease prediction.
  Keywords: autoimmune thyroid disease; deep MLP; cuckoo search optimisation; LDA; weighted decision tree; Bayes linear discriminant analysis; B-LDA; cuckoo search based weighted decision tree; CSWDT.
  DOI: 10.1504/IJBET.2022.10053111
   
  
• An efficient way of identification of protein coding regions of Eukaryotic genes using digital FIR filter governed by Ramanujan’s Sum  
  by Subhajit Kar, Madhabi Ganguly 
  Abstract: Finding protein coding regions, i.e., exons in a gene is a complex problem due to its diverse nature. In this paper, a novel FIR filtering governed by Ramanujan’s Sum is proposed for identification of protein coding regions in gene. The efficacy of the designed algorithms is tested on Caenorhabditis Elegans cosmid F56F11.4a, various benchmark datasets like GENSCAN, HMR195, ASP67, and, BG570, and compared to well-established algorithms based on Antinotch, Butterworth, and Comb filters. The numerical conversion of the biological sequence here is an integer sequence and Ramanujan’s Sum always generates a periodic sequence of integer numbers. This results in reduced quantisation error and simple hardware implementation. The evaluation of the designed Ramanujan’s Sum governed filtering is done at the exonic level, nucleotide level, and through ROC plots. The results obtained on gene F56F11.4 attain specificity of 82%, sensitivity 97%, and precision of 85% while the AUC value of ROC curve was calculated as 0.96 square units. These evaluation parameters reveal that the proposed method gives enhanced results while comparing it to other existing exon-finding techniques.
  Keywords: FIR filter; Ramanujan’s Sum; wavelet transform; exons.
  DOI: 10.1504/IJBET.2022.10053343
   
  
• Exploration of fibro-glandular region and breast density classification of digitized mammograms using least square support vector machine  
  by Vijaya Madhavi Mantragar, Christy Bobby T 
  Abstract: Breast tissue density is one of the significant risk-marker for identification of breast cancer in early stage. In the proposed work, fibro-glandular region is explored and classification of breast density as dense and non-dense is performed. Image pre-processing is performed to improve the image quality followed by segmentation of breast region to obtain region of interest (RoI). For the obtained RoI, pseudo colouring is performed to improve image acuity accompanied by R-image extraction and post-processing to obtain fibro-glandular breast tissues. Area, histogram, fractal, grey-level co-occurrence matrix and grey-level run length matrix features are derived from both fibro-glandular and RoI regions and ratiometric value of features are computed. Further, mutual-information-based feature ranking algorithm is applied on the derived ratiometric values and the significant features are identified. These significant features when fed to least square-support vector machine produced average classification accuracy (%) of 86.1
  Keywords: breast density; pseudo colouring; hue saturation value; HSV; ipsilateral; bilateral; LSSVM.
  DOI: 10.1504/IJBET.2022.10053387
   
  
• Socio-economic Implications of COVID-19 in India: Growing Stress and Educational Challenges  
  by Kiron Jayesh, Mahesh Jayaraman, Visweshwaran Baskaran, Nathiya Narayanaraju, Jagannath Mohan, Adalarasu Kanagasabai 
  Abstract: The global pandemic of COVID-19 has been a challenging period for people all over the world. While the main focus during this period has been to stop the transmission of the disease and increase the vaccination drive, a lot of people are going through unspoken problems on their own. This pandemic brought an imbalance in the well-being of families due to various reasons such as lockdown-related stress or financial instability. To evaluate all these impacts on the students, the female homemakers, and the family relationships, three online surveys were self-administered from various validated questionnaires. The survey concluded that female students (Likert scale 3.234) are more distracted from their classes during the lockdown compared to males (Likert scale 2.458) because females also spend a significant amount of time assisting their families with day-to-day chores. The female members of the family are significantly (p<0.05) more concerned about their familys well-being and relations than the males.
  Keywords: COVID-19; family; female homemakers; financial instability; lockdown; pandemic; stress; students; well-being.
  
  
• A NEW METHOD FOR DIAGNOSING EPILEPSY USING DICTIONARY LEARNING  
  by Ghazal Abbasi, Somayeh Saraf Esmili 
  Abstract: Epilepsy is a disorder of the central nervous system in which the activity of nerve cells in the brain is disrupted and leads to seizures An electroencephalograph is often used to diagnose epilepsy, which records the electrical potential generated in the brain In this study, we aim to diagnose epilepsy from the EEG signals using a new method of dictionary learning and sparse coding Most vital signals have a sparse representation that requires a dictionary to represent the sparse signals In the preprocessing, Butterworth and notch filters are used to remove noises, K-SVD algorithm is used to learn a dictionary to find a matrix of dictionary atoms, and in sparse coding, the orthogonal matching pursuit (OMP) algorithm is used to extract the features from the signals The extracted features were entered as input for classification of signals into two groups of epileptic and non-epileptic signals, using the feature vector machine
  Keywords: Dictionary learning; Epilepsy; K-SVD; Orthogonal matching pursuit (OMP); Sparse representation; Sparse coding; Electroencephalograph (EEG).
  DOI: 10.1504/IJBET.2022.10054052
   
  
• Using artificial intelligence to design healthcare system in IoT  
  by Shipu Jin  
  Abstract: A healthcare system virtual team of IoT is a group of dispersed workers with distinct skills who focus on a specific goal on a temporary or ongoing basis while working in distributed environments, in that way they will lose many work opportunities for information collaboration and knowledge sharing each other. Working in international places further strains teamwork as they have to cope with geographical distance, but also time, culture, and possible linguistic differences. Healthcare system virtual organizations within and across IoT are becoming mature as a potentially effective means for goal-oriented healthcare system teamwork. Among the plentiful support functions enabling efficient cooperation in healthcare system virtual teams, decision-making is a very crucial one but does not find adequate support in contemporary healthcare system software solutions. In this paper, we proposed a healthcare system framework for healthcare system virtual teams in social networks based on the agent-based system. The result of this paper designed a configurable, flexible, and nonintrusive healthcare system virtual software framework.
  Keywords: Healthcare system; IoT; Intelligence agent; Social media; Information sharing.
  DOI: 10.1504/IJBET.2023.10054053
   
  
• Comparative evaluation of geometrical, Zernike moments, and volumetric features of the corpus callosum for discrimination of ASD using machine learning algorithms  
  by Aditi Bhattacharya, Gokul Manoj, Vaibhavi Gupta, Abdul Aleem Shaik Gadda, Dhanvi Vedantham, A. Amilin Prince, Priya Rani, Anandh Kilpattu Ramaniharan, Jac Fredo A. R 
  Abstract: Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with unusual structural changes in brain regions. In this study, we compared the performance of geometrical, Zernike moments, and volumetric features of corpus callosum (CC) to diagnose ASD. The data for the study was obtained from the open-access databases: ABIDE-I and ABIDE-II. Initially, the CC was segmented from the midsagittal view of 2D structural magnetic resonance imaging (sMRI) data using the distance regularized level set evolution (DRLSE). The segmented images were validated with the ground truth using similarity measures. The geometrical and Zernike moments were extracted from the 2D segmented region, and the volumetric features were extracted from 3D images of CC. The features extracted were then used to train support vector machine (SVM), bagging, and random forest (RF) classifiers. The segmented images were highly matched with the ground truth with mean similarity measure values of Sokal and Sneath-II= 0.9928 and Pearson and Heron-II=0.9924, which signified that the DRLSE method was able to segment the CC region successfully. We achieved the highest site-specific classification accuracy of 72.69% using the RF classifier
  Keywords: Autism spectrum disorder; Corpus callosum; sMRI; Level set method; Similarity measures; Geometric features; Volumetric features; Zernike moments; feature selection; Random Forest.
  DOI: 10.1504/IJBET.2022.10054054
   
  
• Integration of radiographic and histological images for the diagnosis of glioblastoma  
  by Fatiha Alim-Ferhat, Linda Ait Mohammed, Mohamed Abdelaziz 
  Abstract: As the number of cancer cases increases, the pathologist’s task becomes increasingly difficult. To classify tumours and define their level of aggressiveness, pathologists are faced with analysing a large number of pathological images, hundreds of thousands of them. Computer-aided methods, including artificial intelligence, can potentially improve tumour classification. It makes sense to implement such a system by taking advantage of the two complementary MRI and histological data. This study proposes to use multiple input convolutional neural networks to predict glioma grade. The proposed method was validated using data from the CPM: RAD-PATH 2020, achieved satisfactory results. We propose a dual path residual convolutional neural network model that trains simultaneously from MRI and pathology images. With this approach, we achieve a validation accuracy of 81%, showing that combining the two image sources yields better overall accuracy.
  Keywords: glioblastoma; digital pathology images; IRM; deep learning; tumour classification.
  DOI: 10.1504/IJBET.2023.10054076
   
  
• Effects of different cushioned insoles on ankle and knee joints biomechanics during load carriage running  
  by Tao Yang, Liangliang Xiang, Shanshan Ying, Jianshe Li, Justin Fernandez, Yaodong Gu 
  Abstract: Load carriage training resulted in substantial injuries among military recruits, particularly in their lower limbs and feet. This study analyzed the phase-specific effects of load carriage with three different material insoles on GRF, angle, and moment of ankle and knee joints during running with military boots. Eighteen male participants were recruited for this study from a local veteran club. A two-way repeated-measures analysis of variance (ANOVA) was conducted to determine statistical effects. The vertical active peak in the ortholite insole group was significantly lower than the control (p=0.002) and cork insoles (p=0.002) with the unloading condition. The control group's ankle dorsiflexion moment was greater than that of the ortholite and cork insoles at zero (p=0.001) and 15 kg load carriage (p=0.001) (46-83% stance). The findings show that the ortholite insole and cork insole improve cushioning performance in the lower limbs and stability of military boots compared with the control insole.
  Keywords: running; load carriage; cushioned insoles; impact force; biomechanics.
  DOI: 10.1504/IJBET.2022.10054077
   
  
• CLAHE Enhanced Hybrid Feature Descriptors for Classification of Acute Lymphoblastic Leukemia in Blood Smear Images  
  by Renuka Tali, Surekha Borra, Vijay Bhaskar Reddy Dinnepu 
  Abstract: Acute lymphoblastic leukemia (ALL) detection through a complete blood count test is often flagged to an expert pathologist for confirmation which is time-consuming, observer-specific, and involve intensive labor. The study proposes an efficient Computer Aided Diagnosis (CAD) method based on image processing and machine learning models to assist doctors in analyzing microscopic images. This study aimed to investigate the combined discriminative qualities of shape and texture features, as well as the best fit feature subset selection technique, to achieve high accuracy and a low false positive rate for classification of healthy and ALL infected leukocyte cell images. The approach begins with preprocessing ALLIDB pictures with the Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement model, followed by feature extraction using Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), the Bag of Visual Words, and Histogram of Oriented Gradients (HOG). The list of the strongest discriminative feature set, as determined by Sequential Forward Selection (SFS) and Principal Component Analysis (PCA), is then utilized to train an SVM machine learning model.
  Keywords: Acute Lymphoblastic Leukemia; Computer Aided Diagnosis; Image Processing; Leukocytes; Machine Learning; Microscopic Images; Feature Extraction; Feature Selection; SVM.
  DOI: 10.1504/IJBET.2023.10054302
   
  
• Prediction of Wear in Total Knee Replacement Implants Using Artificial Neural Network  
  by Vipin Kumar, Anubhav Rawat, Ravi Prakash Tewar 
  Abstract: The current research work presents the development of an artificial neural network (ANN) based model in order to predict the linear wear depth by using wearing parameters such as non-dimensional contact stresses, sliding distance, and cross-shear ratio in the total knee replacement. The linear wear depth values are computed from knee wear models available in literature. The values of linear wear depth obtained from this model were used for training and testing of an artificial neural network model. Multi-layered feed-forward neural network is used for training and testing of the ANN model. Many architectures of neural networks were tried and the 3-6-6-6-1 architecture possessing 3, 6, and 1 neuron in its input layer, every hidden layer, and output layer respectively was found optimum. The sigmoid activation function was chosen for input and hidden layers, and the linear activation function was chosen for the output layer.
  Keywords: Artificial neural network (ANN); Linear wear depth; Total knee replacement; Wear model; and Cross-shear ratio.
  DOI: 10.1504/IJBET.2023.10054459
   
  
• MRI Segmentation Using Deep Learning Network for Brain Tumor Detection  
  by Ambily N, Suresh K 
  Abstract: Gliomas are a combination of infiltrating tumour cells and vasogenic edema. The abscission and radiation intensified in this region will improve survival. It is difficult to distinguish infiltrating cells with conventional imaging sequences. This paper presents an accurate and automatic method for defining areas of tumour infiltration in peritumoral edema in brain MRI, using a fully convolutional neural network, employing Semantic Segmentation technique. The architecture has a contracting path capturing the features and a symmetric expanding path enabling precise localization similar to U-Net. The expansive path yields a U shaped architecture. The multiparametric pattern analysis from clinical MRI sequences assists in identifying the tumor recurrence in peritumoral edema. This helps resection and strengthening of postoperative radiation therapy. In the proposed model, complete core and enhancing regions in Dice Similarity coefficient metric are (0.99, 0.98.0.98).
  Keywords: DNN; Semantic Segmentation; BrainTumour Detection.
  DOI: 10.1504/IJBET.2023.10054461
   
  
• Study of Biomarker Variation and Severity Prediction in Dementia using Intelligent System  
  by AHANA PRIYANKA, G. Kavitha 
  Abstract: Precise detection of dementia biomarkers in the brain enables early understanding of pathology variations. There is a need to study different dementia biomarker in MR images for its specific changes between normal and severity stages to categorize the prognostic difference. This study is an attempt to utilize an optimized framework with fused radiomic and deep features based on least absolute shrinkage and selection operator (LASSO) by using a hybrid meta-heuristic optimizer for classification. The investigation is attempted on ADNI database. The radiomic and deep features are extracted from the considered biomarkers and then fused. Further, the significant features are obtained using LASSO model. Then, these features are given to hybrid meta-heuristic optimizer with machine learning model for classification. Observed results show that hippocampus along with the brainstem gives higher classification accuracy of 97.87% to identify prognostic differences for considered classes. This quantifiable interpretation might improve clinical assessment.
  Keywords: Dementia; hybrid optimizer; fused feature; biomarker and prognostic difference.
  DOI: 10.1504/IJBET.2023.10054579
   
  
• Investigation of photocatalytic effects and extraction of genomic DNA from Staphylococcus aureus through Fe3O4/SiO2/TiO2 magnetic nanoparticles  
  by Farzaneh Firoozeh, Mohammadreza Rezayee Yazdi, Mohammad Zibaei, Hadiseh Rostami, Ali Sobhani Nasab, Azad Khaledi, Farzad Badmasti 
  Abstract: Staphylococcus aureus has been considered as one of the main pathogens that cause various diseases. Therefore, access to fast and reliable DNA-based methods is crucial for the detection and identification of this bacterium. DNA extraction and purification are fundamental primary steps in almost all molecular biology studies. Therefore, the purpose of this work is utilising Fe3O4/SiO2/TiO2 magnetic nanoparticles to extract genomic DNA of Staphylococcus aureus. This paper contains extracting genomic DNA from standard strain of Staphylococcus aureus ATCC 25923 using Fe3O4/SiO2/TiO2 nanostructures. The quality of extracted DNA was evaluated after electrophoresis on gel agarose, also DNA purity and concentrations were measured by a NanoDrop spectrophotometer. The concentration of genomic DNA extracted by Fe3O4/SiO2 magnetic nanoparticles from Staphylococcus aureus strain ATCC 25923 was 131.635 ng/?L. Also, A260/280 and A260/230 values of mentioned DNA were ranged 1.7 to 1.8 and 2 to 2.2 respectively. The obtained results showed that the DNA extracted by the synthesised magnetic nanoparticles has an acceptable concentration and purity for subsequent molecular biology studies in this bacterium.
  Keywords: Fe3O4/SiO2/TiO2; DNA extraction; Staphylococcus aureus; genomic DNA magnetic nanoparticles.
  DOI: 10.1504/IJBET.2023.10054605
   
  
• Multi-resolution dual-encoder self-constrained brain tumor MR image segmentation algorithm  
  by Weijie Hao, Wenyin Zhang, Yong Wu, Yifang Wang, Yuan Qi, Liang Wu, Ji Chen 
  Abstract: : Efficient segmentation of magnetic resonance (MR) brain tumour images is of the utmost implication for the assessment of the condition. Brain tumours proliferate, metastasize quickly, and easily infiltrate surrounding tissues, and there will be magnetic fields, imaging equipment, and patient movements that affect imaging quality during the imaging process. Therefore, automatic brain tumour MR image segmentation has always been among the most challenging scientific research problems. This paper proposes a multi-resolution dual-encoder self-constrained brain tumour MR image segmentation algorithm that can effectively segment the brain tumour lesion area and normal brain tissue. Experiments show that the dice indexes of brain tumour, cerebrospinal fluid, gray matter, and white matter obtained by this algorithm are: 0.91, 0.78, 0.82, and 0.86, respectively. By comparison, the proposed method demonstrates better efficiency and accuracy and has important implications for brain tumour segmentation.
  Keywords: medical image segmentation; brain tumour MR image; multiple resolution dual encoder; CSAM attention decoder; self-constrained network.
  DOI: 10.1504/IJBET.2023.10054646
   
  
• An advanced wavelet decomposition based denoising technique for de-speckling of all ultrasound images  
  by Mayank Singh, Indu Saini, Neetu Sood 
  Abstract: The ultrasound (US) image is well known for accessibility and low cost. Most importantly it is the only diagnostic technique which is radiation free. But, the presence of speckle noise, thoroughly limits its application for diagnosis. This paper aims to remove the noise using wavelet transformation. The US were transformed using discrete wavelet transform after log transformation. A threshold was obtained using the estimated noise variance for each sub-band. A multi-scale thresholding function was proposed to increase the thresholding flexibility. A large range of US were used (12,400, 926, 780, and 1,000 images of fetus, liver, breast and synthetic images respectively) to evaluate the performance. When compared with other thresholding techniques the proposed method has shown a maximum improvement of 172%, 340%, and 380% in peak signal to noise ratio, mean square error, and structural similarity index. With the referenceless metrics our technique has shown 47% improvement in US quality.
  Keywords: denoising; speckle noise; ultrasound images; wavelet transformation.
  DOI: 10.1504/IJBET.2023.10054699
   
  
• FEM-based Fatigue Analysis on a 4-Bar Polycentric Knee of Above-Knee Prostheses  
  by Mohammad-Reza S. Noorani, Saman Hoseini 
  Abstract: Developing safe and low cost artificial lower limbs with long working life is a necessity to help large population of people with amputation to recover the walking ability. So, in this paper we investigate on optimal design of pin components of a 4-bar polycentric knee used in a above-knee prosthesis. The pins suffer cyclic loads and wearing lead to damage and breakage therein, which is early cause of prosthesis failure. Here, we first exploit the ABAQUS to create a finite element model (FEM), then it is integrated with FEsafe software to obtain a fatigue life prediction according to Morrows fatigue criteria. Materials of SUS-304 stainless steel and Ti-6Al-4V titanium alloy are examined to achieve a fatigue life of over 3,000,000 cycles to meet the requirements of ISO 10328:2006. Pins with the diameter of 10 mm satisfies the requirements at all three stages of stance phase, i.e. heel strike, midstance, and push off, when SUS-304 is selected.
  Keywords: Prosthesis; Above-Knee Amputation; Finite Element Method; Stress Analysis; Morrow’s fatigue criteria.
  
  

Special Issue on: Artificial Intelligence for Biomedical and Healthcare systems in IoT

• Sports training on recovery of nerve function and nerve cell apoptosis in athletes with hemorrhagic brain injury  
  by Guoqing Shi 
  Abstract: This article carried out further research on whether sports training will recover after athletes’ brain injury and whether exercise will affect the apoptosis of nerve cells. The method is to reflect the real situation of the athletes by studying experimental mice during the experiment, we selected a total of 60 male and female mice, which were basically similar in weight, and divided them into several groups, and selected two of them as a reference. One group serves as an experiment and one group serves as a control. Each group is divided into seven hours after operation: 5 h, 10 h, 20 h, 40 h, 80 h, 7 d and 14 d. The total number of rats at each time point is 5 through experiments, we can find that the process of sports training is a beneficial process compared with the rats at the same time, and the rats’ recovery of bleeding is more obvious compared with the same.
  Keywords: exercise training; brain injury; nerve function recovery; nerve cell apoptosis; cerebral haemorrhage; medical system.
  DOI: 10.1504/IJBET.2022.10049710