Alzheimer’s Disease Detection Using Optimized Vision Transformer

Abstract

Alzheimer's disease (AD) is a complex, progressive neurodegenerative condition that affects millions of people worldwide, making early diagnosis critical for effective treatment and clinical management to improve quality of life. In this study, we present an automated classification framework based on the Vision Transformer (ViT) model optimized with a modified hippopotamus optimization algorithm (M-HOA). Unlike traditional models that rely solely on ViTs or convolutional networks, the M-HOA algorithm is used to fine–tune key hyperparameters of the ViT model, improving feature extraction and classification accuracy. The model was evaluated on the ADNI dataset, which covers three diagnostic categories (AD, MCI, and NC). Experiments demonstrated that the proposed M-HOA-ViT model outperforms both the baseline and optimized ViT architectures, achieving a classification accuracy of 97.90%. The results indicate that integrating metaheuristic optimization with ViT significantly improves diagnostic accuracy, providing a robust and scalable approach for the early detection of Alzheimer's disease.