Deep learning based automatic diagnoses of attention deficit hyperactive disorder

Abstract

In this paper, we aim to develop a deep learning based automatic Attention Deficit Hyperactive Disorder (ADHD) diagnosis algorithm using resting state functional magnetic resonance imaging (rs-fMRI) scans. However, relative to millions of parameters in deep neural networks (DNN), the number of fMRI samples is still limited to learn discriminative features from the raw data. In light of this, we first encode our prior knowledge on 3D features voxel-wisely, including Regional Homogeneity (ReHo), fractional Amplitude of Low Frequency Fluctuations (fALFF) and Voxel-Mirrored Homotopic Connectivity (VMHC), and take these 3D images as the input to the DNN. Inspired by the way that radiologists examine brain images, we further investigate a novel 3D convolutional neural network (CNN) architecture to learn 3D local patterns which may boost the diagnosis accuracy. Investigation on the hold-out testing data of the ADHD-200 Global competition demonstrates that the proposed 3D CNN approach yields superior performances when compared to the reported classifiers in the literature, even with less training samples.