Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input

• URL: http://arxiv.org/abs/2210.01177v1
• Date: Mon, 3 Oct 2022 18:48:22 GMT
• Title: Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input
• Authors: Zilun Zhang, Farzad Khalvati
• Abstract summary: Do Vision Transformer-based models perform better than CNN-based models? Is it possible to use a shallow 3D CNN-based model to obtain satisfying results? Our results indicate that the shallow 3D CNN-based models are sufficient to achieve good classification results for Alzheimer's Disease using MRI scans.
• Score: 1.0152838128195467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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