Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input
This work addresses Alzheimer's Disease diagnosis for medical imaging applications, but it is incremental as it applies existing methods to a specific domain without major breakthroughs.
The study tackled Alzheimer's Disease classification from 3D MRI scans by comparing Vision Transformer and shallow CNN models, finding that a shallow 3D CNN achieved good results, with the CVVT model reaching 92.5% accuracy and the ConvNet3D-4 model achieving 91.8% accuracy.
Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification. We aim to investigate two relevant questions regarding classification of Alzheimer's Disease using MRI: "Do Vision Transformer-based models perform better than CNN-based models?" and "Is it possible to use a shallow 3D CNN-based model to obtain satisfying results?" To achieve these goals, we propose two models that can take in and process 3D MRI scans: Convolutional Voxel Vision Transformer (CVVT) architecture, and ConvNet3D-4, a shallow 4-block 3D CNN-based model. Our results indicate that the shallow 3D CNN-based models are sufficient to achieve good classification results for Alzheimer's Disease using MRI scans.