International Journal of Biomedical Engineering and Technology (15 papers in press)

Regular Issues

• Hybrid Approach for Skin Lesion Analysis:- Integrating Modified U-Net Segmentation with Vision Transformers for Multi-Class Skin Cancer Detection  
  by Ramya J, Anil Kumar K.M 
  Abstract: Skin cancer is a major global health concern, where early and accurate detection is crucial for patient survival. Traditional CNN-based methods in skin lesion classification face challenges, particularly with the complexity of spatial and semantic features. To address these issues, we propose a unique hybrid deep learning approach integrating vision transformer (ViT) and a modified U-Net model. ViT processes images as tokens instead of pixels, enabling superior feature extraction and classification. Pre-processing techniques, including non-local means filtering for denoising and unsharp masking for contrast enhancement, are applied to enhance model robustness. Our hybrid approach integrates U-Net for precise segmentation, achieving metrics such as IOU of 92.46%, AUC of 97.64%, and dice coefficient of 95.96%. ViT for classification achieves exceptional accuracy, precision, recall, and F1 score, all at 99%. Using the HAM10000 dataset, our method surpasses existing techniques, demonstrating remarkable effectiveness in skin cancer detection and classification.
  Keywords: Skin Cancer Images; Multi-class classification; Vision Transformers; Region-of-Interest Segmentation; Computerized; Medical image processing.
  DOI: 10.1504/IJBET.2025.10069272
   
  
• Alzheimer's Disease Recognition Based on Multimodal Image Fusion  
  by Xinjie Tao, Lisheng Wei, ShengBo Zhu 
  Abstract: To improve diagnostic accuracy for Alzheimer's Disease (AD) and enhance lesion feature extraction from single-modality imaging, a deep learning-based multimodal image fusion classification method is proposed. A novel residual network extracts features from three-dimensional images, serving as a feature extractor for both Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). The extracted features are fused for classification, enhanced by a coordinate attention mechanism to capture spatial and channel relationships in 3D medical images. Experimental results show the fusion network achieved 91.07% accuracy in AD/Mild Cognitive Impairment (MCI)/Cognitively Normal (CN) classification, improving by 7.14% over the basic residual network, 21.43% over single-modality MRI, and 12.5% over single-modality PET. The fusion network also shows performance improvements in AD/CN, AD/MCI, and CN/MCI classification tasks, demonstrating its effectiveness.
  Keywords: Alzheimer's Disease; Multimodal feature fusion; Attention mechanism; Residual network.
  DOI: 10.1504/IJBET.2025.10069613
   
  
• Eye-blink Artifact Removal Framework for EEG Signals using DWT and Autoencoder  
  by Mohd. Faisal, Sudarsan Sahoo, Jupitara Hazarika 
  Abstract: This paper presents an automatic method for removing eye-blink artifacts from contaminated EEG signals, achieving high Signal to Artifact Ratio (SAR) and correlation coefficient (CC) with minimal computational time. In the proposed method, the autoencoder is trained to learn the underlying structure of artifacts within the contaminated frequency band of the EEG signal. Once trained, the autoencoder can effectively remove artifacts from contaminated signal by processing the contaminated signal as its input. Loss function plays important role in reconstructing the input signal. A novel loss function comprising correlation coefficient and mean square error is used to minimize the reconstruction error. The performance of the proposed method is measured by SAR and CC, achieving average values of 3.21 and 0.88, respectively, in removing artifacts from EEG signals. Also, the proposed method achieved minimum computational time in comparison to other methods which is 9 ms.
  Keywords: Brain computer Interface; electroencephalogram; eye-blink artifact; autoencoder.
  DOI: 10.1504/IJBET.2025.10070025
   
  
• Improving Pulmonary Disease Detection through Autoregressive Features and K-Nearest Neighbours Classifier  
  by Alireza Golkarieh, Amirhosein Dolatabadi, Parsa Saei, Omid Rezaei, Fatemeh Dehghani 
  Abstract: The classification of lung sound signals, concerning autoregressive modeling and machine learning algorithms, is the main objective of this study. Lung sounds recorded from 112 subjects with three filter modes (Bell, Diaphragm, Extended) were then classified into two groups: healthy and pulmonary conditions like asthma, COPD, and pneumonia. The AR modelling extracted five essential features for all modes, combined with a classifier using K-Nearest Neighbors (KNN). During training, it gives an accuracy of 98.3% for the unhealthy and 95.5% for the healthy cases, whereas during testing, the results were 100% and 92.3%, respectively. The overall accuracy was 98.2%. Dimensionality was reduced with decreased computational load using the method of AR; therefore, the simple model KNN achieved high accuracy. This efficiency makes the approach suitable for hardware implementation in portable, point-of-care diagnostic devices and thus helps in respiratory disease diagnosis in remote and clinical settings.
  Keywords: Lung sounds; Autoregressive modeling; K-Nearest Neighbors; Pulmonary disease classification; Machine learning.
  DOI: 10.1504/IJBET.2025.10070540
   
  
• Variations in Cardiac Associated Sympathovagal Oscillations under Short-Term Heat Exposure  
  by Kumari Akanksha, Rahul Kumar, Nitya Garg, Yogender Aggarwal, Rakesh Kumar Sinha 
  Abstract: Heat waves alter the autonomic nervous system (ANS) measured through heart rate variability (HRV) and pulse rate variability (PRV). Therefore, the present study aimed to examine the ANS oscillations to significant heat stress stimuli. Digital Lead-II electrocardiogram (ECG) and pulse plethysmogram (PPG) were recorded from control and heat-stressed groups under anesthetized conditions. Tachograms were generated from both ECG and PPG signals. The fast Fourier transform (FFT) was used to analyse the bands using Kubios 3.5.0 software. An unpaired t-test and Bland-Altman test were employed to compare the differences between the two groups. The results demonstrated a significant shift of oscillations toward sympathetic dominance with the withdrawal of parasympathetic activity under heat exposure. The LF oscillations increased and exhibited strong associations between HRV and PRV parameters under the heat stress group. PRV and HRV assess the ANS oscillations and aided with valuable insights into cardiac autonomic function.
  Keywords: Autonomic nervous system; fast Fourier transform; Heat exposure; Heart rate variability; Pulse rate variability; Sympathovagal balance.
  DOI: 10.1504/IJBET.2025.10070752
   
  
• Multilingual Voice Disorder Classification using Glottal Flow and MFCC-based Acoustic Analysis  
  by Nitin Pal, Girish Gidaye, Varsha Turkar, Uma Jaishankar 
  Abstract: Vocal pathologies affect vocal fold dynamics, altering pitch, loudness, and voice quality. Conventional methods rely on invasive techniques. Many researchers have used machine learning models based on features extracted from speech signals. It may not fully capture physiological alterations in vocal fold impairments. To address these challenges, the work in this paper evaluates glottal flow features mined from true voice sources by comparing them against mel-frequency cepstral coefficients (MFCC) based features across four linguistically diverse datasets. The proposed non-invasive method captures most physiological alterations in vocal fold impairments as the features are derived from true voice sources. The data augmentation, oversampling techniques and min-max normalization are employed to overcome dataset limitations and improve model generalization. Sustained vowel /a/ samples are used to train multiple classifiers for each dataset for comparative analysis. It is observed that classifiers using glottal flow features achieved superior performance compared to MFCC.
  Keywords: vocal pathology; classification; glottal flow features; pathological speech analysis; LSTM model; voice disorder detection; healthcare AI applications.
  DOI: 10.1504/IJBET.2025.10071999
   
  
• Analysing EEG Based Differential Functional Connectivity Patterns during Truth and Lie Responses  
  by Sakshi Jethva, Jyoti Maheshwari 
  Abstract: With the growing application of machine learning and deep neural networks in biomedical signal processing, selecting the right features for model training has become crucial especially in lie detection, where accurate classification has serious implications in forensics, law, national security, and research. This study emphasises the importance of feature selection by comparing functional connectivity (FC) networks during truth and lie conditions. Using a publicly available dataset, we applied multiple connectivity measures correlation, phase locking value (PLV), phase lag index (PLI), coherence, and imaginary coherence (iCOH) across frequency bands (global, delta, theta, alpha, beta, gamma). Results showed significantly higher FC during lying in global, delta, and theta bands, particularly in frontal-temporal regions, suggesting their relevance for deception detection. In contrast, alpha, beta, and gamma bands showed inconsistent FC patterns. These findings highlight the complex neural dynamics of lying and support the use of diverse connectivity measures to enhance the accuracy of lie detection systems.
  Keywords: coherence; connectivity; correlation; deception; PLI; PLV.
  DOI: 10.1504/IJBET.2025.10072009
   
  
• The Digital Twin Revolution in Personalised Medicine  
  by Rama Rao Tadikonda, Vasavi Sai Saraswati Rayapudi, Arthika Chauhan Laudia, Saniya Mehrin 
  Abstract: The digital transformation of the health service will be driven by technology under the expanding concept of precision healthcare Future precision medicine is anticipated to embrace personalized diagnostic and treatment plans for each patient, as simulation plays significant part in medicine The advancement of digital twin (DT) technology will render this sort of personalisation possible A digital twin is a virtual representation of a physical entity that has dynamic, mutual connections with its digital counterpart These digital twins have an immense capacity to completely revolutionise healthcare by minimising expenses, raising standards in medical education, research, enhancing patient results and care. The review of this literature discusses the applications of digital twins in medical sector, suggested frameworks, the significance of digital twin for attaining precision healthcare, cyber-security challenges, and ethical consequences for this novel approach which are covered in a greater depth.
  Keywords: Digital Twin; Artificial Intelligence; Cyber technology; Precision healthcare.
  DOI: 10.1504/IJBET.2025.10072020
   
  
• Advances and Future Directions of Foetal Finite Element Modelling in Childbirth: from Biomechanical Interactions to Clinical Implications  
  by Linxiao Shen, Zhenghui Lu, Xin Li, Dong Sun, Yang Song, Gusztáv Fekete, András Kovács, Fan Li, Xuanzhen Cen 
  Abstract: This review aims to summarize the applications of finite element modelling used in labour mechanics and explore their clinical relevance in optimising labour management and intervention strategies. A systematic literature search was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines across the PubMed, IEEE Xplore, Web of Science, and Elsevier ScienceDirect databases. Selected 13 studies was evaluated based on the Methodological Quality Assessment of Single-Subject Finite Element Analysis (MQSSFE). The review highlights the widespread use of whole-body foetal finite element models in childbirth simulations. Key factors in childbirth biomechanics include uterine contraction intensity, abdominal muscle forces, pelvic floor function, foetal head flexion, tissue properties, and descent trajectory. Finite element modelling offers key insights but faces challenges in accuracy, personalised anatomy, and clinical application. Advances in computational biomechanics, imaging validation, and patient-specific simulations will improve childbirth understanding, risk assessment, and labour management.
  Keywords: fetal; finite element; model; childbirth; labor; biomechanics; interactions; clinical; application.
  DOI: 10.1504/IJBET.2025.10072083
   
  
• Utilising Pattern Recognition Algorithm to Determine the Possible Speech Score for Alzheimer's Disease Prediction  
  by Pranab Hazra, Sudipta Banerjee, Kaushik Sarkar, Ashis Kumar Dhara, Tushar Kanti Bera 
  Abstract: This study employs machine learning (ML) to support the early-stage diagnosis of Alzheimers disease (AD) using speech score data as a non-invasive, cost-effective alternative to traditional methods, like imaging and biomarker testing. Conventional techniques are often inaccessible, invasive and lack a strong correlation with cognitive decline. Speech-based assessments capture fine cognitive and linguistic deficits, early indicators of AD. The study employs six cognitive parameters: MoCA v7.1, HVLT-R (immediate and delayed recall), BNT-SF, and WMS-R Logical Memory I and II. A classification model integrating Random Forest, SVM and KNN was evaluated using k-fold cross-validation. ANOVA-based feature selection enhanced model performance, with Random Forest and SVM achieving high AUC scores of 0.98 and 0.97, respectively, outperforming KNN. Among features, MoCA and HVLT-R scores were most critical in distinguishing mild cognitive impairment (MCI) from healthy individuals. The findings highlight the potential of speech scores as early biomarkers for AD detection and monitoring.
  Keywords: Speech Score Data; Physiological Indicator; Non-Invasive Diagnosis; Feature Selection; K-fold cross-validation; Potential Feature; Classification Model; Validation; Area Under Curve (AUC).
  DOI: 10.1504/IJBET.2025.10072541
   
  
• Improving Dental X-ray Image Resolution with Deep Learning-Based Super-Resolution Techniques  
  by Vaishali Patel, Anand Mankodia 
  Abstract: Although the dental X-rays are useful for detecting and treating oral health problems, the low-resolution images they produce often make it hard for dental professionals to see fine details. This limitation occasionally leads to diagnostic challenges and even results in missed problems. As a means of addressing this issue, our study explored the application of deep learning approaches to sharpen and improve the quality of dental X-ray pictures, making them clearer and easier to interpret. We applied several deep learning methods to a dataset of dental X-rays. The results show significant improvements in clarity, with higher image quality scores measured by metrics like PSNR and SSIM that indicate a more detailed view of dental structures. These improvements could help dental professionals to catch issues earlier and make more accurate diagnoses. Our research demonstrates the potential of deep learning to change dental X-ray imaging, supporting better outcomes for patients and providing a useful tool for dental care providers.
  Keywords: Biomedical Image Processing; Super-resolution; Machine Learning; Deep learning; Medical imaging; Image Processing.
  DOI: 10.1504/IJBET.2025.10073088
   
  
• Non-Contact Pulse Rate Estimation from Remote Photoplethysmography using Wavelet Transform Filter and Convolutional Neural Network  
  by Hoang Thi Yen, Doan Van Sang, Van-Phuc Hoang, Guanghao Sun 
  Abstract: Pulse rate (PR) measurement traditionally relies on contact sensors such as photoplethysmography (PPG). However, these are unsuitable during pandemics and for long-term monitoring. Remote photoplethysmography (rPPG) offers contactless cardiovascular monitoring by detecting blood pulsation-induced skin colour changes. While conventional rPPG methods suffer from poor accuracy due to motion and lighting sensitivity, deep learning approaches, though more accurate, require extensive datasets and computational resources while lacking interpretability. This study presents an improved hybrid approach combining conventional preprocessing with deep learning. Video data undergoes traditional processing, band-pass filtering to enhance PR frequencies, and continuous wavelet transform to generate time-frequency images. These feed into a streamlined convolutional neural network (CNN) which is designed to iteratively extract features, resulting in a network that is not overly complex for hardware deployment. Results demonstrate superior performance with 1.9 bpm RMSE, surpassing previous studies through the synergy of traditional preprocessing and CNN-based estimation. This research advances non-contact RGB camera applications in vital sign monitoring.
  Keywords: camera-based; pulse rate; remote photoplethysmography; wavelet filter; convolutional neural network.
  DOI: 10.1504/IJBET.2025.10073137
   
  
• Brain Age Estimation using Cross Entropy Loss and Sparse Autoencoder Features: NeuroAgeNet, a Deep Graph Neural Network  
  by Soumya Kumari L. K, R. Sundarrajan 
  Abstract: MRI brain age evaluations may detect age-related neurological disorders early. Convolutional structures provide global structural information but overlook local neurological changes in standard DL models, restricting their applicability in different populations and high-dimensional neuroimaging. In NeuroAgeNet, DGNN and functional/anatomical brain interregional correlations predict brain age. SAs efficiently reduce high-dimensional input to compact feature representations, improving generality and eliminating redundancy. The regression-based aim solves class imbalance in discrete age groups and evaluates age-related patterns using a modified cross-entropy loss function. Multi-head self-attention and SAG Pool help DGNN layers filter node-level brain connection graphs and capture hierarchical dependencies. Experimental T1- and T2-weighted multi-site structural MRI using IXI dataset. NeuroAgeNet obtained 1.903
  Keywords: Brain Age Estimation; Deep learning; Magnetic Resonance Imaging; Deep Graph Neural Networks; Cross-entropy Loss Function; Sparse Autoencoder; Neuroimaging.
  DOI: 10.1504/IJBET.2025.10073234
   
  
• An Archimedean Spiral Antenna Integrated with an Epsilon Negative Metamaterial Reflector as Microwave Hyperthermia Applicator for Non-Invasive Treatment of Early-Stage Skin Cancer  
  by Komalpreet Kaur, Amanpreet Kaur 
  Abstract: Microwave hyperthermia (MHT) is an emerging non-invasive technique for treating cancer by selectively raising tumour temperatures to therapeutic levels. This manuscript presents the analysis and validation of a MHT applicator for treating both superficial and deep malignancies in the skin. This article shows the analysis and validation of a microwave hyperthermia (MHT) applicator for treating both superficial and deep malignancies in the skin. The proposed applicator consists of an Archimedean spiral micro-strip patch antenna (ASMPA) backed by epsilon-negative (ENG) metamaterial reflector (48
  Keywords: Skin cancer; Archimedean Spiral Antenna; ENG metamaterial; Microwave Hyperthermia; temperature distribution.
  DOI: 10.1504/IJBET.2025.10073238
   
  
• Radiographic Analysis of Foot Bone Alignment Changes across Different High Heel Heights: a Case Study  
  by Hongbin Chang, Meizi Wang, Yang Song, Xuanzhen Cen, Qiaolin Zhang 
  Abstract: While the biomechanical effects of high heels (HHs) have been widely studied, their impact on foot morphology remains unclear. This study assessed variations in foot alignment across heel heights using radiographic analysis. Three healthy female subjects underwent weight-bearing X-rays of the right foot at five heel heights (0, 3, 5, 7, 9 cm) from lateral, anterior-posterior, and hindfoot perspectives. Twenty morphological parameters were measured independently by two investigators. Measurements showed high interobserver reliability. Deviations became evident at 5 cm and increased up to 9 cm. The midfoot demonstrated the greatest change, with arch height rising substantially. The forefoot revealed hallux valgus tendencies, while the hindfoot showed smaller changes but reduced ankle stability. Increasing heel height alters foot bone architecture, promoting deformities that may contribute to foot pathologies with prolonged high-heel use.
  Keywords: Foot morphology; Bone alignment; Radiographic angles; X-ray; High heels.
  DOI: 10.1504/IJBET.2025.10073275