  Alzheimer's is a brain disease that gets worse over time and affects memory,
thinking, and behavior. Alzheimer's disease (AD) can be treated and managed if
it is diagnosed early, which can slow the progression of symptoms and improve
quality of life. In this study, we suggested using the Visual Transformer (ViT)
and bi-LSTM to process MRI images for diagnosing Alzheimer's disease. We used
ViT to extract features from the MRI and then map them to a feature sequence.
Then, we used Bi-LSTM sequence modeling to keep the interdependencies between
related features. In addition, we evaluated the performance of the proposed
model for the binary classification of AD patients using data from the
Alzheimer's Disease Neuroimaging Initiative (ADNI). Finally, we evaluated our
method against other deep learning models in the literature. The proposed
method performs well in terms of accuracy, precision, F-score, and recall for
the diagnosis of AD.
