Leveraging Vision Transformers for Early Detection of Alzheimer's disease Through MRI Image Analysis

Alzheimer's disease (AD) poses challenges for early recognition due to subtle and overlapping symptoms, making timely care and intervention difficult. This work presents a new method for identifying Alzheimer's disease by combining magnetic resonance imaging (MRI) data with a Vision Transformer (ViT) model. In this research, we develop a method that leverages MRI scans to classify Alzheimer's disease (AD) into four distinct stages: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. To improve the identification of clinical and structural alterations in the brain linked to AD, sophisticated characteristics are retrieved, including voxel-intensity patterns, grey matter volume, and cortical thickness. These characteristics allow for a more accurate representation of MRI data when paired with sophisticated processing methods. Additionally, even in situations where data is scarce, transfer learning is used to maximize the Vision Transformer (ViT) model's classification accuracy, especially when it comes to differentiating between neighboring stages of dementia. The ViT model, a cutting-edge deep learning technique, shows strong performance and dependability in identifying AD phases. Our findings highlight its effectiveness, showcasing a significant improvement in diagnostic accuracy compared to existing methods. The accuracy with which the ViT model can distinguish between different phases of AD emphasizes both its suitability for handling complicated MRI data and its promise for early diagnosis and detection.This paper offers a promising method for enhancing MRI image processing for Alzheimer's disease diagnosis through sophisticated feature extraction, transfer learning, and the use of Vision Transformers (ViTs). Present authors believe this method successfully tackles the difficulties of early and precise detection, making it essential for appropriate medical image analysis.