3-D CNN-Based Multichannel Contrastive Learning for Alzheimer’s Disease Automatic Diagnosis

Abstract

Alzheimer’s disease (AD) is a common progressive neurodegenerative disease in the elderly. Mild cognitive impairment (MCI) is the symptomatic predementia stage of AD. Accurately distinguishing AD and MCI patients from normal people is the first step of the disease diagnosis. Several studies have demonstrated the potential of deep learning in the automatic diagnosis of AD and MCI using T1-weighted magnetic resonance imaging (MRI) images. In this article, we proposed an automatic classification method of AD versus normal control (NC) and MCI versus NC based on MRI images. This method used the 3-D convolutional neural network and took the whole 3-D MRI image as the input, which can obtain image information to the greatest extent. In addition, the multichannel contrastive learning strategy based on multiple data transformation methods (e.g., add noise) can combine the supervised classification loss with the unsupervised contrastive loss, which can further improve the classification accuracy and generalization ability of the network. To verify the effectiveness of our method, a large number of experiments were implemented on the ADNI dataset. The results show that our method can achieve excellent performance in accurate diagnosis of AD and MCI; the multichannel contrastive learning strategy can greatly improve the classification accuracy (AD versus NC: 4.19%; MCI versus NC: 4.57%) and generalization ability of the network.