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

TL;DR

This study demonstrates that 3D CNN models can effectively classify schizophrenia patients from healthy controls using structural MRI, outperforming traditional handcrafted feature-based machine learning methods.

Contribution

The paper introduces and compares various 3D CNN architectures for schizophrenia classification, showing superior performance over traditional methods.

Findings

3D CNN models achieved higher accuracy than handcrafted features.

CNN models outperformed traditional machine learning on independent datasets.

The study highlights CNN's potential for imaging-based psychiatric diagnosis.

Abstract

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.