VisTAD: A Vision Transformer Pipeline for the Classification of Alzheimer's Disease

In recent times, the Visual Transformer (VT) has emerged as a powerful alternative to the conventional Convolutional Neural Networks (CNNs) for their superior attention mechanism and pattern recognition abilities. Within a short time, the VT paradigm has given rise to many variants, each showcasing enhanced accuracy and optimized performance for various computer vision applications. Our study introduces a multitransformer pipeline for optimal VT architecture exploration in AD detection and classification. Through a comparative evaluation among the VT variants, this study also aims to contribute valuable insights into the applicability of VTs in Alzheimer's Disease (AD) classification using OASIS and ADNI datasets. Furthermore, VT performances are systematically compared with CNNs to determine the basic capabilities of the models and their limitations in capturing intricate patterns indicative of early AD stages under both data-rich and data-scarce situations. The results resonate with the fact that the attention mechanism of VTs is of pivotal importance for achieving superior performance in AD diagnosis. The codes used in the study are made publicly available.