Self-Supervised Attentive Feature Learning for Alzheimer’s Disease Detection

Hela Elmannai; Nasir Saleem; Sami Bourouis; Reem Ibrahim Alkanhel

doi:10.9781/ijimai.2025.09.002

Authors

Hela Elmannai Princess Nourah bint Abdulrahman University
Nasir Saleem Gomal University
Sami Bourouis Taif University
Reem Ibrahim Alkanhel Princess Nourah bint Abdulrahman University

DOI:

https://doi.org/10.9781/ijimai.2025.09.002

Keywords:

Alzheimer’s Disease, Attentional Network, Brain MRI, Feature Learning, Intelligent Classification, SimCLR, Self-Supervising Learning

Supporting Agencies

This research project was funded by the Deanship of Scientific Research and Libraries, Princess Nourah bint Abdulrahman University, through the Program of Research Project Funding After Publication, grant No (RPFAP-75-1445).

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that leads to memory loss and a decline in cognitive abilities. It primarily affects older adults and is the most common cause of dementia. Using deep learning, models can analyze brain imaging scans to detect specific patterns and biomarkers associated with the disease. Supervised learning models achieve high accuracy rates, but they require a large amount of data sets and labelled medical images. Self-supervised learning can achieve high accuracy rates with fewer training data. This study proposes a self-supervised attentive feature learning network (SSA-Net) for classifying Alzheimer’s disease. The proposed approach leverages self-supervised learning and attention mechanisms to enhance the accuracy and reliability of the classifying model. We employ ResNet-50, incorporating attentive activation, which replaces the ReLU activation, improving the ability of the neural model to focus on the most relevant features in the input medical images. We use SimCLR (Simple Framework for Contrastive Learning of Visual Representations) with the ResNet-50 backbone as a self-supervised learning framework that effectively learns high-quality visual representations in brain MRI (Magnetic Resonance Imaging) scans without labelling. We used the Kaggle Alzheimer’s classification dataset (KACD) containing brain MRI scans for training and testing. Experimental results on the KACD dataset show that the proposed attentive self-supervised ResNet50 reached 99.7% classification accuracy compared to the traditional ResNet50 with 98.1% accuracy. Evaluation metrics show the effectiveness of the proposed SSA-Net for the efficient classification of Alzheimer’s disease.

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Author Biographies

Hela Elmannai, Princess Nourah bint Abdulrahman University

Hela Elmannai received the Ph.D. degree in information technology from SUPCOM, Aryanah, Tunisia, in 2017. She is currently an Associate Professor with the Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia. Her research interests include artificial intelligence, networking, blockchain, and engineering applications

Nasir Saleem, Gomal University

Nasir Saleem received Ph.D. in Digital Speech Processing and Deep Learning from UET Peshawar in 2021. He is currently a Research Fellow at ENU, UK and was a postdoctoral Fellow at IIUM, working on AI-based speech processing algorithms. Previously, he served as a Senior Lecturer (2008–2012) and is now an Assistant Professor at Gomal University. His research interests include Human– Machine Interaction, Speech Enhancement, AV Processing, and Machine Learning. He has published in leading venues (Elsevier, Springer, IEEE) and actively serves as a reviewer for journals.

Sami Bourouis, Taif University

Sami Bourouis received the Engineer, M.Sc., and Ph.D. degrees in computer science from the University of Tunis, Tunisia, in 2003, 2005, and 2011, respectively. He is currently a Professor at the College of Computers and Information Technology, Taif University, Saudi Arabia. His research interests include data mining, image processing, AI, machine learning, cyber security, and pattern recognition applied to several real-life applications.

Reem Ibrahim Alkanhel, Princess Nourah bint Abdulrahman University

Reem Ibrahim Alkanhel received the B.S. degree in computer sciences from King Saud University, Riyadh, Saudi Arabia, in 1996, the M.S. degree in information technology (computer networks and information security) from the Queensland University of Technology, Brisbane, Australia, in 2007, and the Ph.D. degree in information technology (networks and communication systems) from Plymouth University, Plymouth, U.K., in 2019. She has been with Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, since 1997. She is currently a Teaching Assistant with the College of Computer and Information Sciences. Her current research interests include communication systems, networking, the Internet of Things, information security, information technology, quality of service and experience, software-defined networks, and deep reinforcement learning.

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