  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.
