Dynamic Image for 3D MRI Image Alzheimer's Disease Classification. Chapter uri icon


  • 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.

author list (cited authors)

  • Xing, X., Liang, G., Blanton, H., Rafique, M. U., Wang, C., Lin, A., & Jacobs, N.

citation count

  • 23

complete list of authors

  • Xing, Xin||Liang, Gongbo||Blanton, Hunter||Rafique, Muhammad Usman||Wang, Chris||Lin, Ai-Ling||Jacobs, Nathan

Book Title

  • Computer Vision ECCV 2020 Workshops

publication date

  • August 2020