Support vector machine classification combined with multimodal magnetic resonance imaging in detection of patients with schizophrenia. Issue 11 (14th August 2020)

• Record Type: Journal Article
• Authors: Zheng, Yunsong; Tong, Hangbin; Zhao, Teng; Guo, Xiaoxia; Xu, Hui; Yang, Ruwu
• Is Part Of: IET image processing. Volume 14:Issue 11(2020)
• Page Start: 2610
• Page End: 2615
• Publication Date: 2020-08-14
• Subjects: support vector machines -- biomedical MRI -- brain -- medical image processing; multimodal magnetic resonance imaging -- schizophrenia -- schizophrenic patients -- normal human brain avatar -- complex environmental effects -- traditional magnetic resonance imaging -- brain information -- support vector machine classification algorithm -- multimodal MRI detection method -- detection model -- existing test cases -- algorithm model -- brain detection; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1108
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The brain avatar of schizophrenic patients is different from the normal human brain avatar, and it is difficult to overcome the complex environmental effects of the brain through traditional magnetic resonance imaging (MRI). In order to improve the accuracy of MRI in detecting brain information in patients with schizophrenia, this study is based on the support vector machine classification algorithm and combined with multimodal MRI detection method to construct a detection model suitable for patients with schizophrenia. In addition, this study combines the existing test cases to divide the brain into regions and design a comparative experiment to study the accuracy of the model proposed in this study. Finally, the study draws the results by sub‐regional comparison. Studies have shown that the algorithm model of this study has certain effects on brain detection in patients with schizophrenia, and can be applied to practice, and can provide theoretical reference for subsequent related research.