Deep Learning-Based Detection and Diagnosis of Alzheimer’s Disease from MRI Images: A Comparative Approach
DOI:
https://doi.org/10.36602/jsba.2025.20.60الكلمات المفتاحية:
Alzheimer، CNN، Demented، MRIالملخص
Alzheimer’s disease gradually erodes brain function, stringently disrupting memory and reasoning, expressly among older adults. Identifying the condition in its preliminary stages is decisive for timely support and potentially more operative care.This study investigated the application of deep learning models for the automated detection of AD from MRI images. Three Convolutional Neural Network (CNN) architectures are utilized specifically—VGG16, Xception, and ResNet50. The models are evaluated in both binary classification and multi-class classification. Standard evaluation metrics are used to assess model performance. For binary classification, ResNet50 had the highest accuracy (97.96%), followed by VGG16 (97.10%) and Xception (95.93%). In multi-class classification, ResNet50 additionally led (95.39%), slightly ahead of VGG16 (94.92%) and Xception (94.93%).These results underscore the strong potential of ResNet50, in particular, for clinical application, demonstrating reliable generalization to previously unseen MRI images. The study highlights the potential of deep learning models to enhance early detection of Alzheimer’s disease by supporting clinical diagnosis, improving accuracy, and enabling timely interventions. Automated MRI analysis may also reduce costs and expand access to quality screening, especially in resource-limited settings reinforcing the growing case for integrating AI into medical imaging workflows.
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