Early Alzheimer’s Disease Prediction Using Vision Transformers and Attention-Guided MRI Analysis

Year : 2026 | Volume : 16 | Issue : 01 | Page : 30 40
    By

    Altaf O. Mulani,

  1. Professor, Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India

Abstract

Alzheimer’s Disease (AD) continues to be a major global health concern, with early detection being crucial for effective intervention. While conventional machine learning and convolutional neural network (CNN) approaches have made notable progress in automated AD diagnosis using MRI data, they often struggle with capturing long-range dependencies and maintaining spatial contextual awareness. In this research, we propose a novel framework using Vision Transformers (ViTs) for early Alzheimer’s prediction from 3D structural MRI images. The proposed pipeline integrates a preprocessing stage involving skull stripping, voxel normalization, and affine alignment, followed by a Vision Transformer model that processes 2D slices and reconstructs spatial understanding via multi-head self-attention mechanisms. To further enhance learning, we integrate attention-guided saliency maps for region-of-interest identification and use Grad-CAM and SHAP for interpretability. Evaluations were performed on the ADNI dataset using a stratified 5-fold cross-validation approach. Our model achieved 95.1% accuracy, 96.4% precision, 94.9% recall, and a 95.6% F1-score, outperforming baseline CNN models and previous attention-based networks. The attention maps clearly highlighted hippocampal and parietal regions associated with early AD pathology. The study confirms that Vision Transformers, combined with attention-guided interpretability, offer a robust, explainable, and scalable solution for early Alzheimer’s diagnosis using MRI. This architecture holds promise for deployment in clinical decision support systems, particularly in early intervention and treatment planning.

Keywords: Alzheimer’s disease, attention mechanism, medical image analysis, MRI, vision transformers

[This article belongs to Research and Reviews: A Journal of Neuroscience ]

How to cite this article:
Altaf O. Mulani. Early Alzheimer’s Disease Prediction Using Vision Transformers and Attention-Guided MRI Analysis. Research and Reviews: A Journal of Neuroscience. 2026; 16(01):30-40.
How to cite this URL:
Altaf O. Mulani. Early Alzheimer’s Disease Prediction Using Vision Transformers and Attention-Guided MRI Analysis. Research and Reviews: A Journal of Neuroscience. 2026; 16(01):30-40. Available from: https://journals.stmjournals.com/rrjons/article=2026/view=240701


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Regular Issue Subscription Review Article
Volume 16
Issue 01
Received 30/11/2025
Accepted 16/02/2026
Published 19/03/2026
Publication Time 109 Days
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