A gradient boosting machine learning model for neo-epitopes classification - Barde, Romi; Islam, Mohammad; Al Seesi, Sahar (2025 Women in Bioinformatics Workshop (WIBI), 2025)
Keywords: Artificial intelligence; Boosting (machine learning); Computer science; Gradient boosting; Machine learning; Random forest; dept.csc

Incorporating deep learning into capacitive images for smartphone user authentication - Hossain, Md Shafaeat; Islam, Mohammad T.; Akhtar, Zahid (Journal of Information Security and Applications, 2022)
We incorporate deep learning techniques into capacitive images of body parts (ear, four fingers, and thumb) to improve the performance of user authentication in smartphones. Use of a capacitive touchscreen as an image sensor has several advantages, such as it is less sensitive to poor illumination conditions, occlusions, and pose variations. Also, it does not need an additional hardware like iris or fingerprint scanner. Use of capacitive images for user authentication is not new. However, the performance, specially, false reject rates (FRRs) of the state-of-the-art capacitive image-based systems are poor. In this paper, we focus on improving the performance and leverage deep learning. Deep learning techniques demonstrated spectacular performance in previous physical biometrics-based research. However, to our knowledge, effectiveness of deep learning is still unexplored in capacitive touchscreen-based user authentication. In order to bridge this research gap, we devise a multi-modal deep learning model, namely UASNet, and compare its performance with a large set of uni- and multi-modal baselines. Using the UASNet, we achieve an accuracy of 99.77%, an EER of 0.48%, and an FRR of 1.19% at FAR of 0.06%.
0 citations (Crossref) [2023-10-31]
Keywords: Biometrics; CNN; Capacitive images; Deep learning; Fusion; Smartphone; Touchscreen; User authentication; dept.csc; peer-reviewed

Machine learning model for the detection of autism spectrum disorder using electroretinogram signals - Anwar, Muhammad Aqib; Islam, Mohammad Tariqul; Alam, Tanvir (Scientific Reports, 2026)
Keywords: dept.csc

A Deep Learning-Based Approach Towards Simultaneous Localization of Optic Disc and Fovea from Retinal Fundus Images - Islam, Mohammad Tariqul; Ahmed, Ferdaus; Househ, Mowafa; Alam, Tanvir (Studies in health technology and informatics, 2023)
In this work, we propose a multi-task learning-based approach towards the localization of optic disc and fovea from human retinal fundus images using a deep learning-based approach. Formulating the task as an image-based regression problem, we propose a Densenet121-based architecture through an extensive set of experiments with a variety of CNN architectures. Our proposed approach achieved an average mean absolute error of only 13pixels (0.04%), mean squared error of 11 pixels (0.005%), and a root mean square error of only 0.02 (13%) on the IDRiD dataset.
0 citations (Crossref) [2023-10-31]
Keywords: Fovea; Optic Disc; Qatar Biobank; Retina; dept.csc; peer-reviewed

Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning - Islam, Mohammad Tariqul; Ahmed, Ferdaus; Househ, Mowafa; Alam, Tanvir (Studies in health technology and informatics, 2023)
The optical disc in the human retina can reveal important information about a person's health and well-being. We propose a deep learning-based approach to automatically identify the region in human retinal images that corresponds to the optical disc. We formulated the task as an image segmentation problem that leverages multiple public-domain datasets of human retinal fundus images. Using an attention-based residual U-Net, we showed that the optical disc in a human retina image can be detected with more than 99% pixel-level accuracy and around 95% in Matthew's Correlation Coefficient. A comparison with variants of UNet with different encoder CNN architectures ascertains the superiority of the proposed approach across multiple metrics.
0 citations (Crossref) [2023-10-31]
Keywords: CNN; Qatar Biobank; Retina; Segmentation; dept.csc; peer-reviewed

MSLP: mRNA subcellular localization predictor based on machine learning techniques - Musleh, Saleh; Islam, Mohammad Tariqul; Qureshi, Rizwan; Alajez, Nihad; Alam, Tanvir (BMC Bioinformatics, 2023)
Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.
2 citations (Crossref) [2023-10-31]
Keywords: Localization prediction; Machine learning; RNA; Sequence analysis; Subcellular localization; dept.csc; mRNA; peer-reviewed

SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners - AlMulla, Jassim; Islam, Mohammad Tariqul; Al-Absi, Hamada R. H.; Alam, Tanvir (PLOS ONE, 2023)
Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players’ physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players’ performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders’ role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15–30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.
0 citations (Crossref) [2023-10-31]
Keywords: Decision tree learning; Forecasting; Machine learning; Machine learning algorithms; Neural networks; Neurons; Qatar; Sports; dept.csc; peer-reviewed

RDnet: Deep Learning-based model for the Identification of Retinal Detachment - Islam, Mohammad Tariqul; Hafruza, Khadeejath; Musleh, Saleh; Arif, Muhammad; Alam, Tanvir (Proceedings of the 2024 7th International Conference on Healthcare Service Management, 2025)
Retinal Detachment (RD) is one of the major problems with retinal disorder patients. Till to date there existing no confirmatory sign or marker on retina for the early detection of RD. Therefore, patients may have sudden RD at any time of their life. Moreover, it is completely dependent upon the subjective judgement of ophthalmologist to make the final diagnostic decision on RD. To support the decision making process for the ophthalmologist, in this article we proposed RDNet, a SqueezeNet architecture based deep learning model for the early detection of RD. We used publicly available dataset of 1017 images covering rhegmatogenous RD and control group. The proposed model built on this image set achieved 97.55% sensitivity, 99.26% specificity and 98.23% accuracy in detecting RD. The proposed model outperformed the existing models for the same purpose with the highest area under the ROC curve (AUC) of 0.995. We believe our model will support the early detection of RD in clinical setup and assist the ophthalmologist in identifying RD at its early stage.
Keywords: dept.csc

DiaNet: A deep learning based architecture to diagnose diabetes using retinal images only - Islam, Mohammad Tariqul; Al-Absi, Hamada R. H.; Ruagh, Essam A.; Alam, Tanvir (IEEE Access, 2021)
Diabetes is one of the leading fatal diseases globally, putting a huge burden on the global healthcare system. Early diagnosis of diabetes is hence, of utmost importance and could save many lives. However, current techniques to determine whether a person has diabetes or has the risk of developing diabetes are primarily reliant upon clinical biomarkers. In this article, we propose a novel deep learning architecture to predict if a person has diabetes or not from a photograph of his/her retina. Using a relatively small-sized dataset, we develop a multi-stage convolutional neural network (CNN)-based model DiaNet that can reach an accuracy level of over 84% on this task, and in doing so, successfully identifies the regions on the retina images that contribute to its decision-making process, as corroborated by the medical experts in the field. This is the first study that highlights the distinguishing capability of the retinal images for diabetes patients in the Qatari population to the best of our knowledge. Comparing the performance of DiaNet against the existing clinical data-based machine learning models, we conclude that the retinal images contain sufficient information to distinguish the Qatari diabetes cohort from the control group. In addition, our study reveals that retinal images may contain prognosis markers for diabetes and other comorbidities like hypertension and ischemic heart disease. The results led us to believe that the inclusion of retinal images into the clinical setup for the diagnosis of diabetes is warranted in the near future.
18 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85099733279
Keywords: Clinical biomarkers; Convolutional neural network; Convolutional neural networks; Decision making; Decision making process; Deep learning; Diabetes; Diabetes patients; Diagnosis; Feature extraction; Health-care system; Ischemic heart disease; Learning architectures; Licenses; Machine learning models; Medical diagnostic imaging; Medical experts; Medical imaging; Network architecture; Ophthalmology; Qatar; Qatar Biobank (QBB); Retina; Retinopathy; Task analysis; deep learning; dept.csc; diabetes; machine learning; retina

A machine learning based study to assess bone health in a diabetic cohort - Musleh, Saleh; Nazeemudeen, Anjanarani; Islam, Mohammad Tariqul; El Hajj, Nady; Alam, Tanvir (Informatics in Medicine Unlocked, 2022)
Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. Bone mineral density (BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and osteopenia (OPN). The best method for determining bone mineral density and OP/OPN risk is via dual energy X-ray absorptiometry (DXA) technology. The risk of osteoporosis-related fracture may increase for people with diabetes. Therefore, it is necessary to develop a system that can support the early detection of OP/OPN in diabetic patients. In this study, we analyzed Qatar diabetic cohorts including 500 subjects, among which 68 were OP/OPN (target) subjects and 432 were without osteoporosis/osteopenia (control) subjects. The objective of this study is to develop an ML model to distinguish diabetic OP/OPN patients from diabetic non-OP/non-OPN subjects based on their bone health indicators from full body DXA scan measurements. Based on our experiments, AdaBoost model performed the best for classifying the target group from the control group. 10-fold cross validation-based results indicate that the proposed ML model was able to distinguish the target group from the control group at 80% sensitivity, 96% specificity. To the best of our knowledge, our study is the first ML-based approach to detect the early onset of OP/OPN in diabetic cohort from Qatar. Our analyses revealed the higher level of lean mass, fat mass and bone mass for the control group compared to the target group. Higher levels of BMC, BMD from different body parts in the control group compared to the osteoporosis/osteopenia group indicate the protective effects of obesity on bone health in the Qatari diabetic cohort. Moreover, higher value of anthropometric measurements in troch, lumbar spine (L1, L2, L3, L4), pelvis and other body parts in the control group indicates that the WHO guideline can be applied to the Qatari diabetic cohort for the early detection of OP/OPN based on the proposed ML model. Further research on OP/OPN in diabetic patients is warranted in future to confirm the role of DM on bone health.
1 citations (Crossref) [2023-10-31]
Keywords: Bone mineral density; DXA; Diabetes; Dual energy X-ray absorptiometry; Machine learning; Osteopenia; Osteoporosis; Qatar Biobank (QBB); dept.csc; peer-reviewed

Correction: MSLP: mRNA subcellular localization predictor based on machine learning techniques - Musleh, Saleh; Islam, Mohammad Tariqul; Qureshi, Rizwan; Alajez, Nehad M.; Alam, Tanvir (BMC Bioinformatics, 2023)
0 citations (Crossref) [2023-10-31]
Keywords: dept.csc; peer-reviewed

EBAnet: Improved Deep Learning Model for the Detection of Epidermolysis Bullusa Acquisita - Islam, Mohammad Tariqul; Alkhateeb, Mais; Musleh, Saleh; El Hajj, Nady; Alam, Tanvir (2024 6th International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2024)
Epidermolysis bullosa acquisita (EBA) represents a big challenge as a rare skin disorder, with no established markers for early detection for patients. Moreover, as a rare disease, it is extremely difficult to acquire good number of patient sample to diagnose accurately with high confidence. EBA has many biomarkers very similar to other bullosa diseases and needs specific clinical expertise to detect it using immunofluorescence microscopy. In this study, we introduce a deep learningbased method, EBAnet, that leveraged Convolutional Neural Network (CNN) based model for the detection of EBA based on Direct immunofluorescence (DIF) microscopy image. The proposed EfficientNet-based model achieved 97.3% sensitivity, 96.1% precision, and 96.7% accuracy in distinguishing EBA from other class and outperformed the existing model for the same purpose. GradCAM based class activation map also highlighted the important region of the DIF images that was focused by the proposed model leveraging the explainability of the model. We believe, EBAnet will add value in the early and accurate detection of EBA, addressing a critical need in clinical practice.
Keywords: Accuracy; Biological system modeling; Biomarkers; Convolutional Neural Network; Convolutional neural networks; Deep learning; Diseases; Epidermolysis Bullosa Acquisita; Immunofluorescence; Sensitivity; Signal processing; Skin; deep learning; dept.csc

Machine learning model for the identification of lung cancer subtypes based on dna methylation - Al-Qirshi, Raghad; Basit, Syed Abdullah; Musleh, Saleh; Islam, Mohammad Tariqul; Alam, Tanvir (Proceedings of the 2024 7th International Conference on Healthcare Service Management, 2025)
Lung Adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two main histology subtypes of non-small cell lung cancer (NSCLC) with 70% of total Lung Cancer. In this article we proposed an ensemble-based model for the identification of subtypes of NSCLC using methylation data. Proposed Random Forest-based model along with out of bag (OOB) error based feature selection technique identified the top ten most important CpG sites that are highly differentiator between LUSC and LUAD subtypes of NSCLC with an accuracy, precision and F1 Score of \(97\%\) . The proposed model outperformed the other existing models for the same purpose with huge margin of 12%. Pathway analysis of the proposed 10 CpG sites revealed different pathways for LUAD and LUSC associated genes, LUAD-associated genes primarily participated in TP53, PTEN, GLP-1, Incretin regulation, and apoptosis. Conversely, LUSC-associated genes were predominantly involved in pathways for platelet degranulation, serine biosynthesis, and Nephrin family interaction.
Keywords: dept.csc

Diabetic Macular Edema Detection based on Non-mydriatic Retinal Image - Gilbert, Njihia Muchori; Hamada, R. H. Al-Absi; Saleh, Musleh; Mohammad, Tariqul Islam; Tanvir, Alam (2023 3rd International Conference on Computing and Information Technology, 2023)
Keywords: dept.csc

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning - Al-Absi, Hamada R. H.; Islam, Mohammad Tariqul; Refaee, Mahmoud Ahmed; Chowdhury, Muhammad E. H.; Alam, Tanvir (Sensors, 2022)
Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner.
17 citations (Crossref) [2023-10-31] Number: 12
Keywords: DXA; Qatar Biobank (QBB); cardiovascular diseases; deep learning; dept.csc; machine learning; peer-reviewed; retina

DMEgrader: Android Mobile Application for Diabetic Macular Edema Grading Prediction - Al-Absi, Hamada R.H.; Muchori, Gilbert Njihia; Musleh, Saleh a; Islam, Mohammad Tariqul; Pai, Anant; Alam, Tanvir (2023)
More than half a billion people worldwide are affected by diabetes, which is a prevalent non-communicable disease that can lead to critical health conditions, including vision loss. Diabetic Macular Edema (DME) is a primary cause of vision impairment and can eventually lead to blindness in diabetic patients. Early detection of DME and proper health management are crucial to controlling the disease. Retinal image-based AI-enabled diabetes diagnosis has gained significant attention as a non-invasive, fast, and reasonably accurate method for diagnosing DME. To make this technology accessible to underserved communities or areas lacking proper clinical facilities, a mobile application-based solution could have a significant impact. In this article, we describe how we transformed our previously published AI-enabled model into an Android-based mobile application, which is part of a two-phase research study. In the first phase, we developed a deep learning-based model that predicts DME grading using retinal images. In the second phase, we built a mobile application DMEgrader to make our model accessible via a mobile device. To the best of our knowledge, this is the first article to demonstrate necessary steps and code snippets to support developers in transforming deep learning models into Android based mobile applications for DME grading prediction. © 2023 IEEE.
0 citations (Crossref) [2023-10-31]
Keywords: Deep Learning; Diabetes; Diabetic Macular Edema; Qatar Biobank; dept.csc

Risk factors and comorbidities associated to cardiovascular disease in qatar: A machine learning based case-control study - Al-Absi, Hamada R. H.; Refaee, Mahmoud Ahmed; Rehman, Atiq ur; Islam, Mohammad Tariqul; Belhaouari, Samir Brahim; Alam, Tanvir (IEEE access : practical innovations, open solutions, 2021)
Cardiovascular disease (CVD) is reported to be the leading cause of mortality in the middle eastern countries, including Qatar. But no comprehensive study has been conducted on the Qatar specific CVD risk factors identification. The objective of this case-control study was to develop machine learning (ML) model distinguishing healthy individuals from people having CVD, which could ultimately reveal the list of potential risk factors associated to CVD in Qatar. To the best of our knowledge, this study considered the largest collection of biomedical measurements representing the anthropometric measurements, clinical biomarkers, bioimpedance, spirometry, VICORDER readings, and behavioral factors of the CVD group from Qatar Biobank (QBB). CatBoost model achieved 93% accuracy, thereby outperforming the existing model for the same purpose. Interestingly, combining multimodal datasets into the proposed ML model outperformed the ML model built upon currently known risk factors for CVD, emphasizing the importance of incorporating other clinical biomarkers into consideration for CVD diagnosis plan. The ablation study on the multimodal dataset from QBB revealed that physio-clinical and bioimpedance measurements have the most distinguishing power to classify these two groups irrespective of gender and age of the participants. Multiple feature subset selection techniques confirmed known CVD risk factors (blood pressure, lipid profile, smoking, sedentary life, and diabetes), and identified potential novel risk factors linked to CVD-related comorbidities such as renal disorder (e.g., creatinine, uric acid, homocysteine, albumin), atherosclerosis (intima media thickness), hypercoagulable state (fibrinogen), and liver function (e.g., alkaline phosphate, gamma-glutamyl transferase). Moreover, the inclusion of the proposed novel factors into the ML model provides better performance than the model with traditional known risk factors for CVD. The association of the proposed risk factors and comorbidities are required to be investigated in clinical setup to understand their role in CVD better. © 2013 IEEE.
17 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85100930619
Keywords: Anthropometric measurement; Anthropometry; Bioimpedance measurement; Biomarkers; Biomedical measurements; Blood pressure; Cardiology; Cardiovascular disease; Classification (of information); Diagnosis; Disease control; Diseases; Healthy individuals; Intima-media thickness; Machine learning; Multi-modal dataset; Multimodal datasets; Turing machines; dept.csc; peer-reviewed

A pilot study on diabetes detection using handheld fundus camera and mobile app development - Al-Absi, Hamada R. H.; Muchori, Gilbert Njihia; Musleh, Saleh; Basit, Syed Abdullah; Islam, Mohammad Tariqul; Mou, Younss Ait; Alam, Tanvir (Discover Applied Sciences, 2025)
Diabetes, affecting more than 500 million individuals worldwide, is the most widespread non-communicable disease, globally. The early identification and effective management of diabetes are crucial for controlling its spread. Currently, the HbA1c test is the gold standard for the detection of diabetes with high confidence. But this is an invasive, expensive pathology test. Therefore, alternative non-invasive and inexpensive methods have been proposed in the literature for the early detection of diabetes.
Keywords: Android Mobile application; Artificial intelligence; Computer graphics (images); Computer science; Computer vision; DMPred; Diabetes; Diabetes mellitus; Diabetic retinopathy; Fundus (uterus); Fundus camera; Handheld camera; Human–computer interaction; Medicine; Mobile apps; Mobile device; Ophthalmology; Ophthalmoscopy; Retinal; Retinal fundus image; World Wide Web; dept.csc; peer-reviewed

Machine Learning Models Reveal the Importance of Clinical Biomarkers for the Diagnosis of Alzheimer's Disease - Refaee, Mahmoud Ahmed; Ali, Amal Awadalla Mohamed; Elfadl, Asma Hamid; Abujazar, Maha F. A.; Islam, Mohammad Tariqul; Kawsar, Ferdaus Ahmed; Househ, Mowafa; Shah, Zubair; Alam, Tanvir (Importance of Health Informatics in Public Health During a Pandemic, 2020)
Alzheimer's Disease (AD) is a neurodegenerative disease that causes complications with thinking capability, memory and behavior. AD is a major public health problem among the elderly in developed and developing countries. With the growth of AD around the world, there is a need to further expand our understanding of the roles different clinical measurements can have in the diagnosis of AD. In this work, we propose a machine learning-based technique to distinguish control subjects with no cognitive impairments, AD subjects, and subjects with mild cognitive impairment (MCI), often seen as precursors of AD. We utilized several machine learning (ML) techniques and found that Gradient Boosting Decision Trees achieved the highest performance above 84% classification accuracy. Also, we determined the importance of the features (clinical biomarkers) contributing to the proposed multi-class classification system. Further investigation on the biomarkers will pave the way to introduce better treatment plan for AD patients. © 2020 The authors and IOS Press.
WOS:000630065600122
Keywords: ADNI; Adaptive boosting; Alzheimer Disease; Alzheimer disease; Alzheimer's Disease; Alzheimer's disease; Biomarkers; Brain; Classification accuracy; Clinical biomarkers; Clinical measurements; Cognitive Dysfunction; Cognitive impairment; Decision trees; Dementia; Developing countries; Diagnosis; Humans; Machine Learning; Machine learning; Machine learning models; Magnetic Resonance Imaging; Medical informatics; Mild Cognitive Impairment; Mild cognitive impairments (MCI); Multi-class classification; Neurodegenerative diseases; Patient treatment; Public health; biological marker; brain; cognitive defect; dept.csc; human; machine learning; nuclear magnetic resonance imaging