EARLY PREDICTION OF ALZHEIMER'S DISEASE USING CONVOLUTIONAL NEURAL NETWORK
DOI:
https://doi.org/10.4314/njt.2025.4856Keywords:
Alzheimer’s Disease, CNN, Medical Image, MRI, Deep learningAbstract
Alzheimer's disease (AD) is a progressive neurological disorder that shows considerable difficulties in both diagnosis and treatment. Achieving an early and precise diagnosis is crucial for alleviating symptoms and enhancing patient outcomes. Although technological advancements have been made, there is still a pressing need for automated decision-support tools to aid clinicians. Deep learning approaches have shown amazing gains and impressive results in medical image processing this recent years, offering encouraging answers to these problems. In this study, we propose a convolutional neural networks (CNNs) framework trained and tested on a publicly available magnetic resonance imaging (MRI) dataset. We employ extensive data augmentation, dropout regularization, and early stopping to mitigate overfitting. The proposed approach seeks to distinguish between different stages of AD, tackling one of the most intricate aspects of managing the disease. Experimental findings reveal that the proposed model achieved an overall accuracy of 99.3%, with precision, recall, and F1‑score exceeding 98% for all classes, and AUC values over 0.99. This result demonstrates the potential of the framework to delivers superior predictive accuracy and robustness compared to current leading methods, highlighting its potential as a dependable tool for clinical applications in AD detection and prognosis.
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