Using Regularized Multi-Task Learning for Schizophrenia MRI Data Classification

• Published in: Journal of integrative neuroscience Vol. 21; no. 4; p. 119
• Main Authors: Wang, Yu, Shi, Jiantong, Xiao, Hongbing
• Format: Journal Article
• Language: English
• Published: IMR Press 24.06.2022
• Subjects: feature extraction; magnetic resonance imaging; regularized multi-task learning; schizophrenia
• ISSN: 0219-6352
• DOI: 10.31083/j.jin2104119

BACKGROUNDMachine learning techniques and magnetic resonance imaging methods have been widely used in computer-aided diagnosis and prognosis of severe brain diseases such as schizophrenia, Alzheimer, etc. Methods: In this paper, a regularized multi-task learning method for schizophrenia classification is proposed, and three MRI datasets of schizophrenia, collected from different data centers, are investigated. Firstly, slice extraction is used in image preprocessing. Then texture features of gray-level co-occurrence matrices are extracted from the above processed images. Finally, a p-norm regularized multi-task learning method is proposed to simultaneously learn the site-specific and site-shared features of the multi-site data, which can effectively discriminate schizophrenia patients from normal controls. RESULTSThe classification error rate on 10 datasets can be reduced from 10% to 30%. CONCLUSIONSThe proposed method obtains excellent results and provides objective evidence for clinical diagnosis and treatment of schizophrenia.