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