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Convolutional Neural Network (CNN) has been successfully applied onclassification of both natural images and medical images but not yet beenapplied to differentiating patients with schizophrenia from healthy controls.Given the subtle, mixed, and sparsely distributed brain atrophy patterns ofschizophrenia, the capability of automatic feature learning makes CNN apowerful tool for classifying schizophrenia from controls as it removes thesubjectivity in selecting relevant spatial features. To examine the feasibilityof applying CNN to classification of schizophrenia and controls based onstructural Magnetic Resonance Imaging (MRI), we built 3D CNN models withdifferent architectures and compared their performance with a handcraftedfeature-based machine learning approach. Support vector machine (SVM) was usedas classifier and Voxel-based Morphometry (VBM) was used as feature forhandcrafted feature-based machine learning. 3D CNN models with sequentialarchitecture, inception module and residual module were trained from scratch.CNN models achieved higher cross-validation accuracy than handcraftedfeature-based machine learning. Moreover, testing on an independent dataset, 3DCNN models greatly outperformed handcrafted feature-based machine learning.This study underscored the potential of CNN for identifying patients withschizophrenia using 3D brain MR images and paved the way for imaging-basedindividual-level diagnosis and prognosis in psychiatric disorders.

Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls