Dr. Md Khairul Islam
Md Khairul Islam is an Assistant Professor and the Head of the Department of Biomedical Engineering (BME) at Islamic University, Kushtia, Bangladesh. He holds a Ph.D. in Biomedical Engineering, along with M.Sc. and B.Sc. degrees in Information and Communication Technology (ICT) from Islamic University, Kushtia. Dr. Islam is also the Director of the Bio-Imaging Research Laboratory, where he supervises students conducting cutting-edge research. His primary research interests include Bio-Image Processing, Machine Learning, Deep Learning, the Internet of Things, and Health Informatics.
Dr. Khairul Islam has authored over 70 publications in international journals, presented his work at international conferences, and contributed to several book chapters. His research has been cited more than 1,110 times, with an H-index of 14 and an i10-index of 16, according to Google Scholar, Scopus, and ResearchGate. Additionally, he serves as a reviewer for prestigious journals published by Elsevier, Springer, and Hindawi.
Contact
khairul.ice06@gmail.com
Phone No. 01737722198
Google Scholar Profile
Name: Md khairul Islam
Citation Count: 1686
Published Papers:
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An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models
Cited by: 484 | Year: 2021 -
Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm
Cited by: 205 | Year: 2019 -
Brain tumor detection in MR image using superpixels, principal component analysis and template based K-means clustering algorithm
Cited by: 197 | Year: 2021 -
Deep transfer learning approaches for Monkeypox disease diagnosis
Cited by: 117 | Year: 2023 -
Automated detection and characterization of colon cancer with deep convolutional neural networks
Cited by: 67 | Year: 2022 -
Melanoma skin lesions classification using deep convolutional neural network with transfer learning
Cited by: 57 | Year: 2021 -
Cardiovascular disease identification using a hybrid CNN-LSTM model with explainable AI
Cited by: 56 | Year: 2023 -
Enhancing lung abnormalities detection and classification using a Deep Convolutional Neural Network and GRU with explainable AI: A promising approach for accurate diagnosis
Cited by: 51 | Year: 2023 -
Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images
Cited by: 47 | Year: 2024 -
Unlocking the potential of XAI for improved Alzheimer’s disease detection and classification using a ViT-GRU model
Cited by: 45 | Year: 2024 -
A novel approach for best parameters selection and feature engineering to analyze and detect diabetes: Machine learning insights
Cited by: 40 | Year: 2023 -
Alzheimer’s disease detection using m-random forest algorithm with optimum features extraction
Cited by: 37 | Year: 2021 -
Enhancing monkeypox diagnosis and explanation through modified transfer learning, vision transformers, and federated learning
Cited by: 34 | Year: 2024 -
An ensemble classification approach for cervical cancer prediction using behavioral risk factors
Cited by: 33 | Year: 2024 -
Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh
Cited by: 28 | Year: 2024 -
Enhancing lung abnormalities diagnosis using hybrid DCNN-ViT-GRU model with explainable AI: A deep learning approach
Cited by: 27 | Year: 2024 -
Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions--A Systematic Review
Cited by: 26 | Year: 2024 -
Human Brain Tumor Detection using K-means Segmentation and Improved Support Vector Machine
Cited by: 22 | Year: 2020 -
Transfer learning and local interpretable model agnostic based visual approach in monkeypox disease detection and classification: A deep learning insights
Cited by: 18 | Year: 2022 -
Enhancing Atrial Fibrillation detection accuracy: A wavelet transform filtered single lead ECG signal analysis with artificial neural networks and novel feature extraction
Cited by: 16 | Year: 2023