AlzheimerViT: harnessing lightweight vision transformer architecture for proactive Alzheimer’s screening

This paper, along with comprehensive data pre-processing and augmentation, represents one of the major steps toward achieving more robust and clinically applicable models for Alzheimer's disease prediction. The proposed study indicates that deep learning models have the potential to enhance the diagnosis of Alzheimer's disease. By integrating Deep learning techniques with careful data processing, more reliable early detection models can be developed, which in turn leads to better treatment outcomes.