Vision Transformers and Bi-LSTM for Alzheimer's Disease Diagnosis from 3D MRI
This work addresses early diagnosis of Alzheimer's disease for patients, but it is incremental as it combines existing methods on a known dataset.
The authors tackled Alzheimer's disease diagnosis from 3D MRI by proposing a model combining Vision Transformers and Bi-LSTM, achieving high performance in accuracy, precision, F-score, and recall on ADNI data.
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.