Dynamic Image for 3D MRI image Alzheimer’s Disease classification

Xin Xing, Gongbo Liang, Hunter Blanton, M. Usman Rafique, Chris Wang, Ai-Ling Lin, Nathan Jacobs

TL;DR: Training 3D MRI image by 2D CNN model.

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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 dynamic image technology 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/xxx/xxx.

Keywords: Dynamic image, 2D CNN, MRI image, Alzheimer’s Disease