Computer aided Alzheimer's disease diagnosis by an unsupervised deep learning technology

Deep learning technologies have played more and more important roles in Computer Aided Diagnosis (CAD) in medicine. In this paper, we tackled the problem of automatic prediction of Alzheimer's Disease (AD) based on Magnetic Resonance Imaging (MRI) images, and propose a fully unsupervised deep learning technology for AD diagnosis. We first implement the unsupervised Convolutional Neural Networks (CNNs) for feature extraction, and then utilize the unsupervised predictor to achieve the final diagnosis. In the proposed method, two kinds of data forms, one slice and three orthogonal panels (TOP) of MRI image, are employed as the input data respectively. Experimental results run on all the 1075 subjects in database of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1 1.5T) show that the proposed method with one slice data yields the promising prediction results for AD vs. MCI (accuracy 95.52%) and MCI vs. NC (accuracy 90.63%), and the proposed methods with TOP data yields the best overall prediction results for AD vs. MCI (accuracy 97.01%) and MCI vs. NC (accuracy 92.6%). (C) 2019 Elsevier B.V. All rights reserved.