A Novel End-to-End Hybrid Network for Alzheimer's Disease Detection Using 3D CNN and 3D CLSTM

Abstract

Structural magnetic resonance imaging (sMRI) plays an important role in Alzheimer's disease (AD) detection as it shows morphological changes caused by brain atrophy. Convolutional neural network (CNN) has been successfully used to achieve good performance in accurate diagnosis of AD. However, most existing methods utilized shallow CNN structures due to the small amount of sMRI data, which limits the ability of CNN to learn high-level features. Thus, in this paper, we propose a novel unified CNN framework for AD identification, where both 3D CNN and 3D convolutional long short-term memory (3D CLSTM) are employed. Specifically, we firstly exploit a 6-layer 3D CNN to learn informative features, then 3D CLSTM is leveraged to further extract the channel-wise higher-level information. Extensive experimental results on ADNI dataset show that our model has achieved an accuracy of 94.19% for AD detection, which outperforms the state-of-the-art methods and indicates the high effectiveness of our proposed method.