Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI

Abstract

Alzheimer's is a brain disease that worsens over time and affects memory, thinking, and behavior. Alzheimer's disease (AD) can be treated and managed if diagnosed early, slowing the progression of symptoms and improving 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.