​Alzheimer's Disease Classification Using 2D Convolutional Neural Networks

Gongbo Liang,  Xin Xing,  Liangliang Liu,  Yu Zhang,  Qi Ying,  Ai-Ling Lin,  Nathan Jacobs
[arXiv]    [Paper]   [Code] 

Abstract

Alzheimer's disease (AD) is a non-treatable and non-reversible disease that affects about 6% of people who are 65 and older. Brain magnetic resonance imaging (MRI) is a pseudo-3D imaging modality that is widely used for AD diagnosis. Convolutional neural networks with 3D kernels (3D CNNs) are often the default choice for deep learning based MRI analysis. However, 3D CNNs are usually computationally costly and data-hungry. Such disadvantages post a barrier of using modern deep learning techniques in the medical imaging domain, in which the number of data can be used for training is usually limited. In this work, we propose three approaches that leverage 2D CNNs on 3D MRI data. We test the proposed methods on the Alzheimer's Disease Neuroimaging Initiative dataset across two popular 2D CNN architectures. The evaluation results show that the proposed method improves the model performance on AD diagnosis by 8.33% accuracy or 10.11% auROC, while significantly reduce the training time by over 89%. We also discuss the potential causes for performance improvement and the limitation. We believe this work can serve as a strong baseline for future researchers. 

Figure 1. An illustration of different architectures that are used in this study

Recommended citation

@article{xing2021alzheimer,
  title={Alzheimer's Disease Classification Using 2D Convolutional Neural Networks},
  author={Xing, Xin, Liu, Liangliang, Ying, Qi, and Liang, Gongbo},
  journal={2021 43rd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC) },
​  year={2021}
}