AccScience Publishing / BH / Online First / DOI: 10.36922/BH025310043
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ORIGINAL RESEARCH ARTICLE

Early prediction of Alzheimer’s disease using machine learning algorithm: A convolutional neural network approach

Babatunde Hamzat1 ,  Sangita Pokhrel2* ,  Swathi Ganesan2
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1 Department of Data Science, York St John University, London, United Kingdom
2 Department of Computer and Data Science, York St John University, London, United Kingdom
Brain & Heart, 025310043 https://doi.org/10.36922/BH025310043
Received: 28 July 2025 | Revised: 1 October 2025 | Accepted: 9 October 2025 | Published online: 7 November 2025
(This article belongs to the Special Issue The Current and Future Landscape of Alzheimer's Disease Treatment)
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that severely impacts memory and cognitive functions. Early diagnosis remains crucial for timely intervention and care. This research aims to explore the use of artificial intelligence, specifically deep learning, for the early prediction and classification of AD using structural magnetic resonance imaging (MRI) images. A dataset comprising approximately 44,000 brain MRI images with four diagnostic classes (mild, moderate, severe, and very severe dementia) was used to train and evaluate multiple convolutional neural network (CNN) architectures. Three deep learning models were developed and tested: a custom CNN built from scratch, a spatial-channel convolutional attention network (SCCAN), and a pre-trained Visual Geometry Group VGG16 model using transfer learning. The methodology included extensive preprocessing, data augmentation, normalization, and a train–validation–test split to ensure robust performance. Evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrices were used to assess classification efficacy. Among the models tested, the Visual Geometry Group VGG16 model achieved the highest classification accuracy, closely followed by the SCCAN, while the custom CNN demonstrated competitive performance with fewer layers. Grad-CAM visualizations were integrated to provide insight into model decision-making, enhancing interpretability. The results confirm the effectiveness of deep learning in classifying early AD stages with high accuracy and support its integration into clinical diagnostic tools. However, the study also identifies limitations, including dataset diversity, class imbalance, and generalizability across diverse populations. Future research should consider using larger, multi-center datasets (including PET and EEG modalities). This project demonstrates that deep learning can offer reliable, scalable, and interpretable solutions for the early detection of AD, potentially transforming the diagnostic pathway and enabling earlier therapeutic interventions.

Keywords
Alzheimer’s disease
Early detection
Deep learning
Convolutional neural network
Magnetic resonance imaging
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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Brain & Heart, Electronic ISSN: 2972-4139 Published by AccScience Publishing
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