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

TL;DR

This paper introduces a method that transforms 3D MRI images into 2D images using approximate rank pooling, enabling the use of 2D CNNs for Alzheimer's classification with improved accuracy and reduced training time.

Contribution

The authors propose a novel approach to convert 3D MRI data into 2D images for CNN classification, significantly improving efficiency and accuracy over traditional 3D CNNs.

Findings

Achieved 9.5% higher accuracy than baseline 3D models.

Reduced training time to 20% of 3D CNN training.

Demonstrated effective application of 2D CNNs to 3D medical imaging.

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.