DeepAI AI Chat
Log In Sign Up

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

by   Mengjiao Hu, et al.

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.


page 1

page 2

page 3


Lung airway geometry as an early predictor of autism: A preliminary machine learning-based study

The goal of this study is to assess the feasibility of airway geometry a...

Texture Classification of MR Images of the Brain in ALS using CoHOG

Texture analysis is a well-known research topic in computer vision and i...

Prediction of Epilepsy Development in Traumatic Brain Injury Patients from Diffusion Weighted MRI

Post-traumatic epilepsy (PTE) is a life-long complication of traumatic b...

Computational Intelligence Approach to Improve the Classification Accuracy of Brain Neoplasm in MRI Data

Automatic detection of brain neoplasm in Magnetic Resonance Imaging (MRI...

MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features

In this work, we propose bag of adversarial features (BAF) for identifyi...