Identifying Alzheimer’s disease from 4D fMRI using hybrid 3DCNN and GRU networks

Abstract

In recently years, motivated by the excellent performance in automatic feature extraction and complex patterns detecting from raw data, recently, deep learning technologies have been widely used in analyzing fMRI data for Alzheimer’s disease classification. However, most current studies did not take full advantage of the temporal and spatial features of fMRI, which may result in ignoring some important information and influencing classification performance. In this paper, we propose a novel approach based on deep learning to learn temporal and spatial features of 4D fMRI for Alzheimer’s disease classification. This model is composed of 3D Convolutional Neural Network(3DCNN) and recurrent neural network. Experimental results demonstrated that the proposed approach could discriminate Alzheimer’s patients from healthy controls with a high accuracy rate.