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

arXiv: https://arxiv.org/abs/2401.03132 · HTML: https://mlsanity.com/paper/2401.03132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Global Filter Networks for Image Classification](https://mlsanity.com/paper/2107.00645/) (2021)
- [Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification](https://mlsanity.com/paper/1701.06643/) (2017)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network](https://mlsanity.com/paper/1607.00455/) (2016)
- [End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification](https://mlsanity.com/paper/1810.00523/) (2018)
- [Predicting Cognitive Decline with Deep Learning of Brain Metabolism and Amyloid Imaging](https://mlsanity.com/paper/1704.06033/) (2017)
- [Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input](https://mlsanity.com/paper/2210.01177/) (2022)

## Built on by (most cited first, 1 of 1)

- [Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI](https://mlsanity.com/paper/2501.15733/) (2025)

## Abstract (arXiv)

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.