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私たちの研究貢献と科学論文
Showing 47 publication
Ripen Banana Dataset: A Comprehensive Resource for Carbide Detection and Ripeni…
Elman Alam, Md Tarequl Islam, Ishrat Zahan Raka, Onamika Sarkar Ritu, Md Shakhawat Hossain, Wahidur Rahman, Rahat Khan
Data in Brief , Vol. 60
We introduce the “Ripen Banana” dataset, a newly developed collection featuring two distinct classes of ripen banana images: carbide and non-carbide. The dataset contains …
A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empiric…
Md Rajaul Karim, MM Mahbubul Syeed, Ashifur Rahman, Khondkar Ayaz Rabbani, Kaniz Fatema, Razib Hayat Khan, Md Shakhawat Hossain, Mohammad Faisal Uddin
Scientific Data (Nature) , Vol. 12 (1)
Assessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known …
Predicting the effect of Bevacizumab therapy in ovarian cancer from H&E whole s…
Md Shakhawat Hossain, Munim Ahmed, Md Sahilur Rahman, MM Mahbubul Syeed, Mohammad Faisal Uddin
Intelligence-Based Medicine , Vol. 11 (100231)
Ovarian cancer (OC) ranks fifth in all cancer-related fatalities in women. Epithelial ovarian cancer (EOC) is a subclass of OC, accounting for 95 % of …
AnxPred: A Hybrid CNN-SVM Model with XAI to Predict Anxiety among University St…
Md. Rajaul Karim, MM Mahbubul Syeed, Kaniz Fatema, Md. Shakhawat Hossain, Razib Hayat Khan and Mohammad Faisal Uddin
Perceived anxiety is a prevalent issue among university students, negatively affecting both mental health and academic outcomes. Prompt evaluation of anxiety triggered by academic factors …
Automated Gleason Grading of Prostate Cancer from Low-Resolution Histopathology…
Md Shakhawat Hossain, Md Sahilur Rahman, Munim Ahmed, Anowar Hussen, Zahid Ullah, Mona Jamjoom
Computers, Materials & Continua
One in every eight men in the US is diagnosed with prostate cancer, making it the most common cancer in men. Gleason grading is …
Comparison of CNN and Transformer Models for Predicting the Effects of Anti-VEG…
Galib Muhammad Shahriar Himel, Munim Ahmed, Shamsun Nahar Shatabdy, Md Sahilur Rahman, Md Shakhawat Hossain, MM Mahbubul Syeed and Mohammad Faisal Ud…
Anti-vascular endothelial growth factor (anti-
VEGF) therapy, such as Bevacizumab, treats colorectal, lung,
kidney, and breast cancer patients. In 2018, it was approved for
treating ovarian cancer (OC) …
Ensemble of Deep Learning Models to Select Ovarian Cancer Patients for Bevacizu…
Md Sahilur Rahman; Munim Ahmed; Md Shakhawat Hossain; MM Mahbubul Syeed; Mohammad Faisal Uddin
Bevacizumab monoclonal therapy, in combination with chemotherapy, is an FDA-approved treatment for ovarian cancer (OC), particularly for patients with epithelial ovarian cancer (EOC) patients. …
From survey to solution: A deep learning framework for reliable monkeypox diagn…
Md Shakhawat Hossain, Munim Ahmed, Md Sahilur Rahman
Array , Vol. 28
Monkeypox, a re-emerging zoonotic disease, poses a global health threat due to its rapid transmission and visual similarity to other skin lesions such as chickenpox, measles and acne. Deep learning methods, which detect monkeypox from skin images, offer a promising solution to overcome the limitations of manual and PCR-based diagnoses, which are time-consuming, error-prone and impractical in low-resource settings. However, existing methods are limited by poor dataset quality, weak generalizability and inconsistent benchmarking. Practical issues, such as lesion variability, image noise, unsuitable augmentations and minimal preprocessing, pose further challenges to clinical deployment. This study addressed these issues through a three-fold contribution: a comprehensive survey of deep learning methods analyzing their strengths and limitations; the development of a diverse, clinically representative benchmark dataset to better assess model generalizability; and a robust deep learning ensemble framework that improved diagnostic accuracy across diverse skin images. The proposed ensemble method incorporated practical classes, a noise-free and balanced dataset, clinically relevant augmentations and effective preprocessing steps, achieving over 95% accuracy across major public datasets to ensure robustness and readiness for clinical deployment. Explainability analysis using Shapley Additive exPlanation (SHAP) confirmed the method’s reliability across all skin tones and body parts. A paired t-test showed that the ensemble model performed significantly better than individual models across four public datasets (𝑝 = 0.005, Cohen’s 𝑑 = 2.38).
SmartFarming: Lumpy Skin Disease Detection from Smartphone-captured Cattle Imag…
Sharmin Islam Shroddha; Sanjana Raquib Bijoya; Towsif Ahmed; Umme Rumman Chaity; Md Shakhawat Hossain; M M Mahbubul Syeed
Lumpy skin disease, commonly known as LSD, poses a significant threat to cattle health, welfare and the livestock industry. It is associated with substantial …
Vision Transformer for the Categorization of Breast Cancer from H&E Histopathol…
Md Shakhawat Hossain, Ashifur Rahman, Munim Ahmed, Kaniz Fatema, MM Mahbubul Syeed, Mohammad Anowar Hussen and Mohammad Faisal Uddin
Journal of Image and Graphics , Vol. 13 (4) , pp. 380-393
Breast Cancer (BC) is the most frequent form of cancer, accounting for 24.5% of all cancer cases worldwide, with projections estimating 364,000 cases by …
AI-assisted polyps detection to facilitate autonomous endoscopy examination
Munim Ahmed, Md Sahilur Rahman, Md Shakhawat Hossain, Mahmudur Rahman, Fariha Karim, Mahreen Tabassum
Examining the gastrointestinal (GI) tract to find polyps is a major task in endoscopy to diagnose, monitor and treat digestive disorders and diseases. Traditionally, …
BDFreshFish: A Comprehensive Image Dataset of Bangladeshi Freshwater Fishes to …
Md. Wahidur Rahman, Md. Tarequl Islam, Md. Rahat Khan, Mohammad Motiur Rahman, Munim Ahmed, Md Shahilur Rahman, Md Shakhawat Hossain
The BDFreshFish dataset that is presented in the paper contains image data that corresponds to eight different types of native freshwater fish that are …
Ripen Banana Dataset: A Comprehensive Resource for Carbide Detection and Ripening Stage Analysis to Enhance Food Quality and Agricultural Efficiency
Elman Alam, Md Tarequl Islam, Ishrat Zahan Raka, Onamika Sarkar Ritu, Md Shakhawat Hossain, Wahidur Rahman, Rahat Khan
Data in Brief , Vol. 60
We introduce the “Ripen Banana” dataset, a newly developed collection featuring two distinct classes of ripen banana images: carbide and non-carbide. The dataset contains images from raw to ripe bananas that have been ripened with carbide and without carbide. …
A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted Research
Md Rajaul Karim, MM Mahbubul Syeed, Ashifur Rahman, Khondkar Ayaz Rabbani, Kaniz Fatema, Razib Hayat Khan, Md Shakhawat Hossain, Mohammad Faisal Uddin
Scientific Data (Nature) , Vol. 12 (1)
Assessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface …
Predicting the effect of Bevacizumab therapy in ovarian cancer from H&E whole slide images using transformer model
Md Shakhawat Hossain, Munim Ahmed, Md Sahilur Rahman, MM Mahbubul Syeed, Mohammad Faisal Uddin
Intelligence-Based Medicine , Vol. 11 (100231)
Ovarian cancer (OC) ranks fifth in all cancer-related fatalities in women. Epithelial ovarian cancer (EOC) is a subclass of OC, accounting for 95 % of all patients. Conventional treatment for EOC is debulking surgery with adjuvant Chemotherapy; however, in 70 % …
AnxPred: A Hybrid CNN-SVM Model with XAI to Predict Anxiety among University Students
Md. Rajaul Karim, MM Mahbubul Syeed, Kaniz Fatema, Md. Shakhawat Hossain, Razib Hayat Khan and Mohammad Faisal Uddin
Perceived anxiety is a prevalent issue among university students, negatively affecting both mental health and academic outcomes. Prompt evaluation of anxiety triggered by academic factors is essential to promote student wellness and academic success. Recent studies have incorporated Machine Learning …
Automated Gleason Grading of Prostate Cancer from Low-Resolution Histopathology Images Using an Ensemble Network of CNN and Transformer Models
Md Shakhawat Hossain, Md Sahilur Rahman, Munim Ahmed, Anowar Hussen, Zahid Ullah, Mona Jamjoom
Computers, Materials & Continua
One in every eight men in the US is diagnosed with prostate cancer, making it the most common cancer in men. Gleason grading is one of the most essential diagnostic and prognostic factors for planning the treatment of prostate …
Comparison of CNN and Transformer Models for Predicting the Effects of Anti-VEGF Drugs on Ovarian Cancer from Histopathology Images
Galib Muhammad Shahriar Himel, Munim Ahmed, Shamsun Nahar Shatabdy, Md Sahilur Rahman, Md Shakhawat Hossain, MM Mahbubul Syeed and Mohammad Faisal Uddin
Anti-vascular endothelial growth factor (anti-
VEGF) therapy, such as Bevacizumab, treats colorectal, lung,
kidney, and breast cancer patients. In 2018, it was approved for
treating ovarian cancer (OC) patients; however, when administered, it results in some adverse effects. Therefore, this therapy is given …
Ensemble of Deep Learning Models to Select Ovarian Cancer Patients for Bevacizumab Monoclonal Therapy
Md Sahilur Rahman; Munim Ahmed; Md Shakhawat Hossain; MM Mahbubul Syeed; Mohammad Faisal Uddin
Bevacizumab monoclonal therapy, in combination with chemotherapy, is an FDA-approved treatment for ovarian cancer (OC), particularly for patients with epithelial ovarian cancer (EOC) patients. However, numerous studies have reported adverse effects associated with this treatment, including hypertension, bleeding, cardiac …
From survey to solution: A deep learning framework for reliable monkeypox diagnosis using skin images
Md Shakhawat Hossain, Munim Ahmed, Md Sahilur Rahman
Array , Vol. 28
Monkeypox, a re-emerging zoonotic disease, poses a global health threat due to its rapid transmission and visual similarity to other skin lesions such as chickenpox, measles and acne. Deep learning methods, which detect monkeypox from skin images, offer a promising solution to overcome the limitations of manual and PCR-based diagnoses, which are time-consuming, error-prone and impractical in low-resource settings. However, existing methods are limited by poor dataset quality, weak generalizability and inconsistent benchmarking. Practical issues, such as lesion variability, image noise, unsuitable augmentations and minimal preprocessing, pose further challenges to clinical deployment. This study addressed these issues through a three-fold contribution: a comprehensive survey of deep learning methods analyzing their strengths and limitations; the development of a diverse, clinically representative benchmark dataset to better assess model generalizability; and a robust deep learning ensemble framework that improved diagnostic accuracy across diverse skin images. The proposed ensemble method incorporated practical classes, a noise-free and balanced dataset, clinically relevant augmentations and effective preprocessing steps, achieving over 95% accuracy across major public datasets to ensure robustness and readiness for clinical deployment. Explainability analysis using Shapley Additive exPlanation (SHAP) confirmed the method’s reliability across all skin tones and body parts. A paired t-test showed that the ensemble model performed significantly better than individual models across four public datasets (𝑝 = 0.005, Cohen’s 𝑑 = 2.38).
SmartFarming: Lumpy Skin Disease Detection from Smartphone-captured Cattle Images using Transformer Model
Sharmin Islam Shroddha; Sanjana Raquib Bijoya; Towsif Ahmed; Umme Rumman Chaity; Md Shakhawat Hossain; M M Mahbubul Syeed
Lumpy skin disease, commonly known as LSD, poses a significant threat to cattle health, welfare and the livestock industry. It is associated with substantial economic loss and cattle death, especially in Bangladesh, where most farmers do not have proper …
Vision Transformer for the Categorization of Breast Cancer from H&E Histopathology Images
Md Shakhawat Hossain, Ashifur Rahman, Munim Ahmed, Kaniz Fatema, MM Mahbubul Syeed, Mohammad Anowar Hussen and Mohammad Faisal Uddin
Journal of Image and Graphics , Vol. 13 (4) , pp. 380-393
Breast Cancer (BC) is the most frequent form of cancer, accounting for 24.5% of all cancer cases worldwide, with projections estimating 364,000 cases by 2040. Accurate diagnosis and effective categorization of BC are essential for proper treatment planning, patient …
AI-assisted polyps detection to facilitate autonomous endoscopy examination
Munim Ahmed, Md Sahilur Rahman, Md Shakhawat Hossain, Mahmudur Rahman, Fariha Karim, Mahreen Tabassum
Examining the gastrointestinal (GI) tract to find polyps is a major task in endoscopy to diagnose, monitor and treat digestive disorders and diseases. Traditionally, an expert performs this examination to identify polyps based on the endoscope-generated images displayed on …
BDFreshFish: A Comprehensive Image Dataset of Bangladeshi Freshwater Fishes to Develop AI-driven Solutions for Autonomous Fisheries Management
Md. Wahidur Rahman, Md. Tarequl Islam, Md. Rahat Khan, Mohammad Motiur Rahman, Munim Ahmed, Md Shahilur Rahman, Md Shakhawat Hossain
The BDFreshFish dataset that is presented in the paper contains image data that corresponds to eight different types of native freshwater fish that are found in various parts of Bangladesh. The following categories are distinguished by their scientific names: …