Alzheimer’s disease classification from MRI using vision transformer

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that presents significant challenges for early diagnosis and intervention. Traditional approaches for diagnosing AD using MRI images are labor-intensive and often subjective, resulting in the need for automated, accurate solutions to support clinicians in early-stage detection. This study investigates the use of vision transformer (ViT) for the classification of Alzheimer's disease stages using MRI images. By treating MRI images as sequences of tokens, ViT models capture both global and local spatial dependencies, which enhances their ability to recognize structural brain changes characteristic of AD. The model was trained on a diverse dataset containing four AD categories – Moderate Demented, Mild Demented, Very Mild Demented, and Non-Demented – achieving an overall classification accuracy of 98.9%. This result highlights the efficacy of transformer-based models in distinguishing between subtle structural brain alterations. Future directions for this study include fine-tuning the model on larger datasets and exploring the integration of multi-modal data to further support AD diagnosis and treatment strategies. The findings indicate that vision transformer have the potential to transform diagnostic imaging for neurodegenerative disorders by providing a robust, scalable, and precise tool for early AD detection.