Classification of Schizophrenia versus normal subjects using deep learning

Abstract

Motivated by deep learning approaches to classify normal and neuro-diseased subjects in functional Magnetic Resonance Imaging (fMRI), we propose stacked autoencoder (SAE) based 2-stage architecture for disease diagnosis. In the proposed architecture, a separate 4-hidden layer autoencoder is trained in unsupervised manner for feature extraction corresponding to every brain region. Thereafter, these trained autoencoders are used to provide features on class-labeled input data for training a binary support vector machine (SVM) based classifier. In order to design a robust classifier, noisy or inactive gray matter voxels are filtered out using a proposed covariance based approach. We applied the proposed methodology on a public dataset, namely, 1000 Functional Connectomes Project Cobre dataset consisting of fMRI data of normal and Schizophrenia subjects. The proposed architecture is able to classify normal and Schizophrenia subjects with 10-fold cross-validation accuracy of 92% that is better compared to the existing methods used on the same dataset.