Deep Learning Architecture for Early Alzheimer’s Disease Detection

Abstract

Alzheimer’s disease represents a degenerative brain condition that gradually worsens over time, severely affecting mental abilities and making it difficult to identify and treat in its early stages. Recent breakthroughs in artificial intelligence have transformed how medical images are analyzed, offering powerful tools for precise and automatic detection of Alzheimer’s disease. To address this challenge, this study proposes a deep learning approach using two convolutional neural network (CNN) architectures: a VGG16-based transfer learning model and a lightweight custom CNN. Models were trained on MRI datasets with preprocessing and data augmentation techniques to improve robustness. Results demonstrate that the custom CNN achieves superior accuracy (95%) compared to the VGG16 (90%) approach while maintaining computational efficiency. The findings indicate significant potential for automated AD detection in clinical applications, contributing to AI-driven diagnostic tools for neuroimaging.