Use of 3D-CAPSNET and RNN models for 4D fMRI-based Alzheimer’s Disease Pre-detection

Abstract

Predicting Alzheimer's disease (AD) at an early stage can assist more successfully prevent cognitive decline. Numerous investigations have focused on utilizing various convolutional neural network (CNN)-based techniques for automated diagnosis of AD through resting-state functional magnetic resonance imaging (rs-fMRI). Two main constraints face the methodologies presented in these studies. First, overfitting occurs due to the small size of fMRI datasets. Second, an effective modeling of the 4D information from fMRI sessions is required. In order to represent the 4D information, some studies used the deep learning techniques on functional connectivity matrices created from fMRI data, or on fMRI data as distinct 2D slices or 3D volumes. However, this results in information loss in both types of methods. In order to model the spatiotemporal (4D) information of fMRI data for AD diagnosis, a new model based on the capsule network (CapsNet) and recurrent neural network (RNN) is proposed in this study. To assess the suggested model's effectiveness, experiments were run. The findings show that the suggested model could classify AD against normal control (NC) and late mild cognitive impairment (lMCI) against early mild cognitive impairment (eMCI) with accuracy rates of 94.5% and 61.8%, respectively.