Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 57 references that are in the map. First posted on arXiv 3 Oct 2022.
Builds on
22 of 57 references.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.