Deep Learning-Based Early Detection of Alzheimer’s Disease from Brain MRI Scans

International Journal of Emerging Research in Science, Engineering, and Management

Vol. 2, Issue 3, pp. 396–403, March 2026

https://doi.org/10.58482/ijersem.v2i3.50

This work is licensed under a Creative Commons Attribution 4.0 International License .

Deep Learning-Based Early Detection of Alzheimer’s Disease from Brain MRI Scans

J Meena, B Bhavya Sri, J Dhanush Yadav, R Dileep, K Bharath

Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, AP, India.

Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder characterized by irreversible deterioration of cognitive and functional abilities, resulting in a significant burden on patients, caregivers, and healthcare systems. Early detection of structural brain changes plays a critical role in enabling timely intervention, individualized treatment planning, and evaluation of disease-modifying therapies. This study presents a deep learning–based framework for automated detection of Alzheimer’s disease using structural magnetic resonance imaging (MRI). The proposed approach incorporates standardized preprocessing of volumetric MRI scans, discriminative feature extraction using a customized convolutional neural network architecture, and classification of subjects into clinically relevant diagnostic categories. To address challenges associated with limited and imbalanced datasets, data augmentation and regularization strategies are applied to improve model generalization capability. Model performance is evaluated using standard classification metrics including accuracy, sensitivity, specificity, and F1-score on curated subsets of publicly available MRI datasets. Experimental observations indicate that the proposed framework effectively captures disease-related morphological patterns and demonstrates strong potential as a computer-aided support tool for early-stage Alzheimer’s disease assessment.

Keywords: Alzheimer’s disease, Deep learning, Convolutional neural network, Brain MRI, Medical image analysis.

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DOI: https://doi.org/10.58482/ijersem.v2i3.50

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Issue 3 (March 2026)

2026-03-31