Back to Top

About

Dr. Mohammad Abu Yousuf received the B.Sc.(Engineering) degree in Computer Science and Engineering from Shahjalal University of Science and Technology, Sylhet, Bangladesh in 1999, the Master of Engineering degree in Biomedical Engineering from Kyung Hee University, South Korea in 2009, and the Ph.D. degree in Science and Engineering from Saitama University, Japan in 2013. In 2003, he joined as a Lecturer in the Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh. In 2014, he moved to the Institute of Information Technology, Jahangirnagar University. He is now working as Professor at the Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh. His research interests include Medical Image Processing, Human-Robot Interaction, and Computer Vision, Natural Language Processing.

View More >>

Skills

Experience

Assistant Professor

Jahangirnagar University (JU)

Dec-2019 to Present

Publication

  • dott image October, 2024

Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but i...

  • dott image October, 2024

Bias in Deep Learning Skin Cancer Detection: Parallel Residual Convolution Network Classification and Racial Bias Quantification

Journal : International Conference on Computing Advancements - ICCA 2024

Globally, skin cancer remains one of the most popular and lethal forms of cancer, significantly affecting the death rate. Several studies have been carried out regarding the automatic identi...

  • dott image August, 2024

ASDNet: A robust involution-based architecture for diagnosis of autism spectrum disorder utilising eye-tracking technology

Journal : IET Computer Vision

Autism Spectrum Disorder (ASD) is a chronic condition characterised by impairments in social interaction and communication. Early detection of ASD is desired, and there exists a demand for t...

Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study

Sleep apnea (SA) is one of the most prevalent sleep-related problems, impacting more than 100 million people worldwide. A full-night Polysomnography (PSG) is an effective SA diagnosis strate...

Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study

Sleep apnea (SA) is one of the most prevalent sleep-related problems, impacting more than 100 million people worldwide. A full-night Polysomnography (PSG) is an effective SA diagnosis strate...

Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor

Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment options, while accurate classification and segmentation of brain tumors are vital for developing per...

  • dott image January, 2024

ASDNet: A robust involution-based architecture for diagnosis of autism spectrum disorder utilising eye-tracking technology

Journal : IET Computer Vision

Autism Spectrum Disorder (ASD) is a chronic condition characterised by impairments in social interaction and communication. Early detection of ASD is desired, and there exists a demand for t...

  • dott image August, 2023

SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization

The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilitie...

  • dott image August, 2023

SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization

The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilitie...

GRU-INC: An inception-attention based approach using GRU for human activity recognition

Human Activity Recognition (HAR) is very useful for the clinical applications, and many machine learning algorithms have been successfully implemented to achieve high-performance results. Al...