Improving Alzheimer's Diagnosis using Vision Transformers and Transfer Learning

Alzheimer's disease is a neurodegenerative disorder defined by memory loss and primarily affects older individuals. Currently, there is no definitive cure available. Although medications are accessible, they only serve to slow the progression of the disease. In this paper, we propose the use of Vision Transformers and Transfer Learning for Alzheimer's classification. Our approach leverages the temporal aspect of the transformer to model the correlation between different image patches. Transfer learning enables us to mitigate the issue of insufficient available data. Our method has been validated on the OASIS dataset, which consists of 250 brain scans. The results demonstrate that transfer learning with Transformer models surpasses the performance of transfer learning with CNN models by 4% and exceeds traditional CNN models without transfer learning by 8%. Two types of Transformers were tested: ViT-B16 and ViT-B32. The results are comparable, with ViT-B32 outperforming ViT-B16 by 1%.