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

11/30/2020

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by   Xin Xing, et al.

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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 time compared to 3D CNN models. The code is available online: https://github.com/UkyVision/alzheimer-project.