Early Identification of Alzheimer's Disease Using an Ensemble of 3D Convolutional Neural Networks and Magnetic Resonance Imaging

Alzheimer's disease (AD) has become a nonnegligible global health threat and social problem as the world population ages. The ability to identify AD subjects in an early stage will be increasingly important as disease modifying therapies for AD are developed. In this paper, we propose an ensemble of 3D convolutional neural networks (en3DCNN) for automated identification of AD patients from normal controls using structural magnetic resonance imaging (MRI). We first employ the anatomical automatic labeling (AAL) cortical parcellation map to obtain 116 cortical volumes, then use the samples extracted from each cortical volume to train a 3D convolutional neural network (CNN), and finally assemble the predictions made by well-performed 3D CNNs via majority voting to classify each subject. We evaluated our algorithm against six existing algorithms on 764 MRI scans selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our results indicate that the proposed en3DCNN algorithm is able to achieve the state-of-the-art performance in early identification of Alzheimer's Disease using structural MRI.