• 2025-04-25
• I.D. Oladipo, P.O. Adebayo, R.O. Yusuff, M. Abdulraheem, G.B. Balogun
• Volume 1, Issue 1

Abstract

Early detection of Alzheimer’s disease (AD) is critical for timely intervention and effective management. This study introduces a robust conventional neural network (CNN) model designed to classify magnetic resonance imaging (MRI) scans into four categories representing different stages of Alzheimer’s Disease progression: Non Demented, Very MildDemented, Mild Demented, and Moderate Demented. Leveraging a dataset of 6400 labeled MRI images, we employed advanced preprocessing techniques including normalization and data augmentation to optimized model training. The CNN model achieved an impressive accuracy of 95%, with a sensitivity of 93% and specificity of 90%, surpassing several benchmark models. These findings underscore the potential of CNNs as a promising tool for accurate and automated Alzheimer’s disease diagnosis. Future research will explore integrating additional biomarkers and expanding the model’s diagnostic capabilities to include more nuanced stages of AD progression, promising to reshape the landscape of neurodegenerative disease diagnostics.

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