Abstract

Info

Title:: Application of regularized multi-task learning in schizophrenia MRI data classification

Author(s):: ZHANG Na; WANG Yu; ZHOU Wen; XIAO Hongbing; XING Suxia; Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China

Keywords:: ; Keywords: schizophrenia; magnetic resonance imaging; feature extraction; regularized multi-task learning

Abstract:: Abstract: Machine learning techniques and magnetic resonance imaging (MRI) techniques have been used in the analysis of MRI data of patients with mental diseases in various regions to achieve computer-aided diagnosis and prognosis of mental diseases such as schizophrenia, etc. Herein slice extraction is firstly used for MRI image preprocessing. Then texture features of gray-level co-occurrence matrices are extracted from the above processed images. Finally, a lp-norm regularized multi-task learning method based on support vector machine for MRI data classification is proposed to simultaneously learn the site-specific and site-shared features of schizophrenia images from 3 data centers, which can be used to discriminate schizophrenia patients from normal controls. Experiments show that the proposed method achieves a high diagnosis accuracy, providing a biological basis for the clinical diagnosis and treatment of schizophrenia.