A Multi-Modal Attention-Guided Network for Alzheimer's Disease Classification Using Deep Learning

Authors

  • Mahmoud Baniata Department of Computer Science, Faculty of Information Technology, Applied Science Private University, Amman, Jordan
  • Suhaila Abuowaida Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al al-Bayt University, Mafraq, Jordan
  • Mohammad Aljaidi Department of Cyber Security, Zarqa University, Zarqa, Jordan
  • Mohammad Kharabsheh Department of Computer Information Systems, Faculty of Prince Al-Hussein bin Abdullah II of Information Technology, The Hashemite University, Zarqa, Jordan
  • Ayoub Alsarhan Department of Information Technology, The Hashemite University, Zarqa, Jordan
  • Amjad A. Alsuwaylimi Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia
Volume: 15 | Issue: 5 | Pages: 27150-27158 | October 2025 | https://doi.org/10.48084/etasr.12510

Received: 2 June 2025 | Revised: 11 July 2025, 18 July 2025, 20 July 2025, and 28 July 2025 | Accepted: 1 August 2025 | Online: 6 October 2025


Corresponding author: Amjad A. Alsuwaylimi

Abstract

Alzheimer's Disease (AD) is a chronic neurodegenerative disease that affects a large portion of the global population, and early and accurate diagnosis is the key component to proper management and treatment. This work presents the MAGNet model, a novel Deep Learning (DL) architecture for AD classification based on multi-modal imaging data. The MAGNet model uses multi-modal attention that is capable of hierarchically fusing structural MRI, functional MRI, and PET scans to obtain different kinds of information. The MAGNet model was tested on three datasets, with an overall accuracy of 96.2% when distinguishing between the AD, MCI, and CN groups. The proposed approach surpasses benchmark models by achieving 3.5% better accuracy and 5.2% higher sensitivity for early MCI diagnosis. Moreover, the MAGNet model offers interpretable results, employing attention visualization to support clinicians' decisions. MAGNet has the potential to predict cognitive scores and brain age with MMSE errors of 1.8 and a brain age of 2.3 years.

Keywords:

multi-modal, attention, Alzheimer's disease, deep learning, ResNet

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[1]
M. Baniata, S. Abuowaida, M. Aljaidi, M. Kharabsheh, A. Alsarhan, and A. A. Alsuwaylimi, “A Multi-Modal Attention-Guided Network for Alzheimer’s Disease Classification Using Deep Learning”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 5, pp. 27150–27158, Oct. 2025.

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Copyright (c) 2025 Mahmoud Baniata, Suhaila Abuowaida, Mohammad Aljaidi, Mohammad Kharabsheh, Ayoub Alsarhan, Amjad A. Alsuwaylimi

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