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

Regular Issues

• Automated COVID-19 Detection from Chest X-ray and CT Images Using Optimised Hybrid Classifier  
  by Madhavi Bhongale, Pauroosh Kaushal, Renu Vyas 
  Abstract: Amidst the global threat of infectious diseases, exemplified by COVID-19, conventional RT-PCR detection methods are time-consuming and potentially misleading. This study introduces an innovative approach, utilising CT and X-ray images as markers for efficient COVID-19 detection. An automatic assessment tool, integrating V-SLBT and GLCM features, optimises image texture analysis for precise classification by a deep belief network (DBN). Enhancing accuracy, a hybrid BWUCOA is integrated into DBN. The tool's workflow involves image pre-processing, optimal texture feature computation, and DBN-based classification. Validation with clinical data from 82 patients attests to a 98% accuracy. Comparative analysis reveals a 1.32% improvement for X-ray and a 2.38% enhancement for CT images over existing methods, underscoring the efficacy of V-SLBT and BWUCOA in refining the classifier's accuracy. This swift and cost-effective tool provides a precise diagnosis for COVID-19.
  Keywords: COVID-19 detection; CT image; chest X-ray image; GLCM; SLBT feature; deep belief network; DBN; black widow updated coronavirus optimisation algorithm; BWUCOA.
  DOI: 10.1504/IJBET.2023.10062228
   
  
• Classifying Muscle Performance of Junior Endurance and Power Athletes using Machine Learning  
  by Maisarah Sulaiman, Aizreena Azaman, Noor Aimie Salleh, Muhammad Amir As`ari, Izwyn Zulkapri 
  Abstract: This paper aimed to characterise the muscle performance of endurance and power athletes using machine learning approach. In this regard, electromyography (EMG) features were extracted from the vastus lateralis muscle and used to feed the support vector machine (SVM) classifier. The performance of various EMG features was evaluated based on their classification accuracy, sensitivity, specificity, and F-score. The accuracy was the highest for the feature set selected using the feature selection approach compared to the single feature. Specifically, the performance of sequential backward selection (SBS) was superior to the sequential forward selection (SFS) approach. Meanwhile, based on the SVM classification result, the radial basis function (RBF) kernel performed better than the other investigated kernel types, such as linear, polynomial, and sigmoid kernel. This muscle characterisation of endurance and power athletes may be useful as a muscle-monitoring tool for future talent identification and talent development, particularly in young athletes.
  Keywords: electromyography; EMG; muscle; athlete; endurance; power; distance runner; sprinter; classification; machine learning.
  DOI: 10.1504/IJBET.2023.10062238
   
  
• Gabor Fully Convolutional Network and Ellipse Fitting Technique for Fetal Head Segmentation and Biometry Measurement  
  by Ahmed Zaafouri, Hanene Sahli, Radhouane Rachdi, Mounir Sayadi 
  Abstract: This paper introduces a new approach for foetal head segmentation and biometry measurement based on Gabor fully convolutional networks (G-FCN) along with the ellipse fitting technique. A fully convolutional network (FCN) training process based on Gabor features is presented. The new approach tends to accelerate the training stage and gives successful results. The Gabor wavelets with their steerable properties (i.e., their scales and orientations) are able to reinforce the robustness of G-FCN and reduce the training complexity. The proposed model is applied for foetal US image segmentation and foetal head circumference (HC) measurement using the elliptical fit technique. Our datasets are provided from a radiographic sequence of the foetus during different periods of pregnancy. An experimental study is conducted to prove the usefulness of the proposed algorithm for foetal biometric purposes. In addition, the automated approach makes it easier for doctors to diagnose US images.
  Keywords: ultrasound images; ellipse fitting; fully convolutional network; Gabor CNNs; Gabor wavelets.
  DOI: 10.1504/IJBET.2023.10062348
   
  
• EMG Scalogram-Based Classification of Gait Disorders Using Attention-Based CNN: A Comparative Study of Wavelet Functions  
  by Pranshu C.B.S. Negi, Balendra ., Shubrendu Shekhar Pandey, Shiru Sharma, Neeraj Sharma 
  Abstract: This study aims to classify gait abnormalities caused by rheumatoid arthritis and prolapsed intervertebral disc using scalograms from the EMG signals. Classifying EMG signals is difficult because of their variability, high dimensionality, and sensor placement. We propose to bridge this gap by using the wavelet transform and attention-based neural networks. The study involved five participants: one with rheumatoid arthritis, two with prolapsed intervertebral disc, and two healthy subjects. The proposed methodology uses four different wavelet functions: complex Gaussian, frequency B Spline, Mexican Hat, and Shannon, to construct scalograms, and an attention-based CNN for classification. A comparison of performance of the proposed algorithm with nine machine learning classifiers: K nearest neighbour, Na
  Keywords: electromyography; EMG; convolution neural networks; attention networks; scalogram; gait analysis.
  DOI: 10.1504/IJBET.2024.10062509
   
  
• A Statistical Shape Modeling Method for Predicting the Human Head from the Face  
  by Vi-Do Tran, Phong-Phu Vo, Ngoc-Lan-Nhi Tran, Tien-Tuan Dao, Tan-Nhu Nguyen 
  Abstract: Predicting the back head based only on the face is necessary for generating the full head and skull. We prepared a dataset of 329 surface meshes of the head. These meshes were reconstructed and post-processed from computed tomography (CT) image sets of adult subjects having normal head structures. A novel back head and face sampling technique was also developed for acquiring back head and face features. The relation between the face features and the back head features was trained using four strategies: non-rigid scaling, SSM optimization, partial least squares regression (PLSR), and principal component analysis (PCA). A ten-fold cross-validation procedure was conducted for selecting the optimal training strategies and tuning their parameters. The face features and the predicted back head features formed the head. The mean mesh-to-mesh distances between the predicted and the ground truth back head were (mean
  Keywords: face-to-head prediction; statistical shape modelling; head-to-skull prediction; biomechanical head simulation; partial least squares regression; PLSR; principal component analysis; PCA.
  DOI: 10.1504/IJBET.2023.10062592
   
  
• Early Prediction of Heart Disease Risk using eXtreme Gradient Boosting: A Data-Driven Analysis  
  by Hamdi Al-Jamimi 
  Abstract: Heart disease is a leading cause of morbidity and mortality worldwide. Early identification of heart disease risk is critical for timely treatment and prevention of further complications. This study provides a detailed examination of a novel heart disease dataset encompassing 333 cases and 21 features. The study employed the eXtreme gradient boosting (XGBoost) algorithm to develop an intelligent predictive model to detect the likelihood of heart disease at an early stage. The choice of the XGBoost model for this study was apt, considering its strengths in managing structured medical datasets with multiple features, resistance to overfitting, and interpretability for insights into feature importance. Feature selection was utilised to identify the most important predictors for prediction. The findings demonstrate that the Gradient Boosting classifier outperforms other machine learning (ML) techniques with a 99% accuracy rate. The results highlight the capability of ML in aiding the early detection of heart diseases.
  Keywords: heart disease; healthcare; artificial intelligence; AI; machine learning; ML; early prediction.
  DOI: 10.1504/IJBET.2024.10062625
   
  
• The Multifaceted Applications of Al2O3 Nanoparticles in Biomedicine: A Comprehensive Review  
  by Vinayakprasanna Hegde 
  Abstract: Aluminium oxide nanoparticles (Al2O3 NPs) have emerged as a promising class of nanomaterials with diverse biomedical applications. Their unique physicochemical and mechanical properties, biocompatibility, and ease of functionalization have led to extensive research exploring their potential in various biomedical fields. This review paper comprehensively summarizes the recent advances in the biomedical applications of Al2O3 NPs, encompassing drug delivery systems, tissue engineering, bioimaging, and diagnostic platforms etc. The discussion focuses on the synthesis methods and surface modifications that enhance the efficacy and biocompatibility of Al2O3 NPs. Additionally, the review shedding light on their potential toxicological implications and biodegradability. Overall, this paper provides valuable insights into the current state of research on Al2O3 NPs in the biomedical domain, fostering advancements in healthcare and medical technologies.
  Keywords: Biomedicine; nanoparticles; Al2O3; toxicity.
  DOI: 10.1504/IJBET.2024.10062831
   
  
• S-R2F2U-Net: A single-stage model for teeth segmentation  
  by Mrinal Kanti Dhar, Mou Deb 
  Abstract: Precision tooth segmentation is crucial in the oral sector because it provides location information for orthodontic therapy, clinical diagnosis, and surgical treatments. In this paper, we investigate residual, recurrent, and attention networks to segment teeth from panoramic dental images. Based on our findings, we suggest three models: single recurrent R2U-Net (S-R2U-Net), single recurrent filter double R2U-Net (S-R2F2U-Net), and single recurrent attention enabled filter double (S-R2F2-Attn-U-Net). Particularly, S-R2F2U-Net, as emphasised in the title, outperforms state-of-the-art models in terms of accuracy and dice score. A hybrid loss function combining cross-entropy loss and dice loss is used in training. In addition, it reduces around 45% of model parameters compared to the original R2U-Net. Models are trained and evaluated on the UFBA-UESC dataset that contains 1,500 extra-oral panoramic X-ray images and divided into ten categories based on the structural variations. S-R2F2U-Net achieves 97.31% accuracy and 93.26% dice score. Codes are available on https://github.com/mrinal054/teethSeg_sr2f2u-net.
  Keywords: tooth segmentation; semantic segmentation; deep learning; recurrent module; attention module.
  DOI: 10.1504/IJBET.2024.10063262
   
  
• Modeling and simulation of electroencephalography (EEG) instrumentation to study the epileptic seizure: A noise analysis approach  
  by Sunil Choudhary, Tushar Kanti Bera 
  Abstract: Electroencephalography (EEG) is extremely useful for diagnosing and treating various brain diseases and disorders. An EEG instrumentation which consists of analogue amplifiers, filters, digitisers, and data acquisition system, plays a crucial role in designing efficient EEG acquisition-hardware for acquiring low-amplitude EEG signals. This paper presents a comprehensive simulation study conducted on the various EEG instrumentation and their noise analysis to design a high gain and low noise EEG amplifier at low-cost. Different EEG-amplifier circuits are developed in NI-Multisim and noise-analysis has been studied to identify the best EEG-measurement system. The simulation results show that the EEG amplifier developed with AD8428 and OP07 shows the highest gain (22 8k), high SNR (70.93 dB), and high CMRR (136 dB) within a low noise level for the EEG signal bandwidth. The present work provides a guideline for designing EEG circuits with high gain and low-noise levels to acquire brain signals for neuroscientific studies.
  Keywords: epileptic seizures; electroencephalography; EEG; low-cost and low-noise EEG instrumentation; circuit simulation; high CMRR; high SNR; noise analysis.
  DOI: 10.1504/IJBET.2024.10063349
   
  
• Alterations of generic musculoskeletal models to incorporate realistic knee joint and muscle geometry for biomechanical analyses during healthy gait: A Narrative Literature Review  
  by Shivangi Giri, Ravi Prakash Tewari 
  Abstract: Knee is an important weight-bearing 6-degree-of-freedom (DOF) joint that is essential for stable locomotion. However, the majority of lower limb musculoskeletal (MSK) models only include one DOF and hence fail to represent the true biomechanics of the knee. For models with multi-DOF knee anatomically realistic modelling of muscle architecture is crucial in representing the mechanical stability of the entire limb. The purpose of this narrative review, therefore, was to report state-of-the-art knowledge on the existing generic rigid-body MSK models that have incorporated: 1) multi-DOF knee; 2) multi-line characterisation of muscle geometry, to analyse normal healthy human gait. 15 studies accommodated multi-DOF knee joint, however majority of them retained the single-line oversimplified muscle geometry. Those that focused on better characterising muscle geometry (n = 8) used only single-DOF knee joints. Most importantly, this review showed that no generic MSK model exists that incorporates realistic representations of both knee and muscle volume.
  Keywords: musculoskeletal modelling; knee; muscle redundancy; review; biomechanics.
  DOI: 10.1504/IJBET.2024.10063535
   
  
• A Deep Learning approach for the augmented diagnosis and prediction of infectious Lung Diseases  
  by Geetha R, Umarani Srikanth, Kamalanaban E 
  Abstract: The pandemic coronavirus is an alarming threat to public health nowadays causing severe acute lung and bronchial infection that incurs a high fatality rate in humans. Researchers vigorously work in this area to find solutions for this critical issue by all means. On the other hand, tests to be carried out to determine the survival time of coronavirus infection across different communities of the population is a long-term need. Herein, this research describes a robust deep neural network to diagnose the suspicious patient’s chest X-ray (CXR) in detecting the presence of infection rapidly. This simple and rapid scalable approach has the capacity of immediate application in coronavirus diagnosis as well as predicting the spread and infection probability for every individual put under-diagnosis depending upon their health and societal parameters. Our robust deep neural network yields the best result of 97.87% accuracy and is user-friendly compared to existing methods.
  Keywords: deep learning; convolutional neural network; CNN; chest X-ray; CXR; rectified linear units; ReLUs; infectious lung disease.
  DOI: 10.1504/IJBET.2024.10063639
   
  
• Development and Validation of Virtual Reality Combined with Shoulder Wheel Device for Active Rehabilitation Training  
  by Mohhamad Reza Hosseini, Hamid Khabiri, Hamid Sharini, Vahab Dehlaghi, Ali Safarpoor 
  Abstract: In light of prevalent shoulder impairments among stroke survivors, this study aimed to evaluate the impact of a virtual reality-based rehabilitation system on enhancing active rehabilitation time during shoulder exercises. Thirty stroke patients were divided into two groups: one received conventional shoulder wheel therapy combined with virtual reality, while control group received only shoulder wheel therapy. Clinical assessments, including action research arm test and Fugl-Meyer Assessment, along with functional testing (torque) were conducted three times over a 15-day interval, with a reaction rate test administered at the end of rehabilitation. Results demonstrated that virtual reality-based rehabilitation significantly improved torque, reaction rate, and the Fugl-Meyer Assessment functional test compared to the control group (p<0.05). However, no significant difference was observed in the Action Research Arm Test assessments. This study suggests that virtual reality can play a crucial role in enhancing shoulder functions and increasing active rehabilitation time for stroke patients.
  Keywords: motor impairment; shoulder wheel; elbow and shoulder rehabilitation; virtual reality; motivational environment; active rehabilitation.
  DOI: 10.1504/IJBET.2024.10063958
   
  
• Improved Diagnosis of Lung Cancer Classification based on Deep learning Method  
  by Amel Feroui, Meriem Saim, Mohammed El Amine Lazouni, Sihem Amel Lazzouni, Zineb Aziza Elaouaber, Mahammed Messadi 
  Abstract: Lung cancer, a globally impactful and severe disease, affects millions worldwide. Uncontrolled growth of abnormal lung tissue cells results in severe complications and high mortality. Early detection is crucial for improved prognosis and survival rates. This study presents two methods for lung cancer classification utilising computed tomography (CT) images, which offer detailed scans of the lungs. The first method employs the VGG16 and VGG19 deep learning architectures. The second method utilises pre-trained VGG16 and VGG19 models for feature extraction, followed by training of supervised learning algorithms SVM, k-NN, and decision (DT) for classification. Evaluation of the proposed methods was conducted on two publicly available databases: the LIDC-IDRI database and the IQ-OTH/NCCD database. The results demonstrated that the VGG19 architecture outperforms the VGG16 architecture in terms of accuracy and precision across both databases. However, VGG16 excels on a hybrid database. Additionally, the k-NN classifier outperforms the SVM and decision tree classifiers, indicating the superiority of transfer learning over deep learning for lung cancer image classification. The proposed system has potential implications for improving patient outcomes through early detection and diagnosis.
  Keywords: lung cancer; CT scan images; deep learning; machine learning; biomedical engineering; artificial intelligence; medical imaging; classification; LIDC-IDRI database; IQ-OTH/NCCD database.
  DOI: 10.1504/IJBET.2024.10064213
   
  
• Surface and Hardness Profiles Of Additively and Conventionally Manufactured CoCrMo Alloy in Dental Application: A Preliminary Analysis  
  by Ahmad Syamil Shahruddin, Muhammad Hussain Ismail, Aini Hayati Abdul Rahim 
  Abstract: Denture has long been a viable and versatile option to replace missing teeth. Denture frameworks are made of cobalt-based alloy conventionally fabricated through casting. As technology evolves, additive manufacturing is used to manufacture metal products allowing denture fabrication to be more predictable. Many aspects of additively manufactured denture frameworks require investigation for it to be clinically acceptable. In this study, denture framework samples from SLM and cast manufacturing underwent sample preparation and were analysed for surface roughness using Alicona infinite focus and hardness tests using Vickers hardness tester, and Mitutoyo MVK-H1. The powder and the raw ingot were analysed for better understanding of the material's microstructure. This study aims to compare the surface roughness and hardness profile of cobalt-chromium denture frameworks produced by metal casting and additive manufacturing. A comparison of hardness and surface roughness for various manufacturing techniques is presented.
  Keywords: additive manufacturing; alloy denture framework; CoCrMo; hardness; surface roughness; selective laser melting.
  DOI: 10.1504/IJBET.2024.10064260
   
  
• A Systematic Review of Deep Learning Algorithms Utilising Electroencephalography Signals for Epileptic Seizure Detection  
  by Sunil Choudhary, Tushar Kanti Bera 
  Abstract: Researchers are diligently endeavouring to integrate artificial intelligence (AI) into medical practice to harness the most recent breakthroughs in these fields. Early identification and accurate disease prediction are the primary goals of healthcare to administer the efficient preventative care at any disease or critical illness like epilepsy. The epilepsy is found as a condition that is marked by repeated and unpredictable seizure-activity. The difficulty of accurately predicting epileptic seizures has not been fully solved yet. Recently, the AI tools, have been utilised to help the doctors by providing the disease information extracted from the patient';s datasets. This paper discusses the applications of deep learning algorithms (DLA) for epileptic seizure detection utilising electroencephalography (EEG) signals. The significant obstacles associated with accurately detecting automated epileptic seizures have also been studied using DLA in conjunction with EEG-data. It also analyses the advantages, challenges and limitations of the DLA applied for epileptic-seizure detection (ESD).
  Keywords: EEG signals; classification; deep learning; DL; machine learning; ML; epileptic seizures; detection and diagnosis; deep learning algorithms; DLA.
  DOI: 10.1504/IJBET.2024.10064735