Dynamic Image for 3D MRI Image Alzheimer's Disease Classification

• URL: http://arxiv.org/abs/2012.00119v1
• Date: Mon, 30 Nov 2020 21:39:32 GMT
• Title: Dynamic Image for 3D MRI Image Alzheimer's Disease Classification
• Authors: Xin Xing, Gongbo Liang, Hunter Blanton, Muhammad Usman Rafique, Chris Wang, Ai-Ling Lin, Nathan Jacobs
• Abstract summary: Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. Our proposed CNN model achieves $9.5%$ better Alzheimer's disease classification accuracy than the baseline 3D models.
• Score: 26.296422774282156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally expensive. We make use of approximate rank pooling to transform the 3D MRI image volume into a 2D image to use as input to a 2D CNN. We show our proposed CNN model achieves $9.5\%$ better Alzheimer's disease classification accuracy than the baseline 3D models. We also show that our method allows for efficient training, requiring only 20% of the training time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.

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