Analysis of schizophrenia using support vector machine classifier

被引：0
作者：
G. Wiselin Jiji
A. Rajesh
Ajitha Kanagaraj
机构：
[1] Dr. Sivanthi Aditanar College of Engineering,Department of Computer Science & Engineering
[2] Scientist,Department of Computer Science & Engineering
[3] Vikram Sarabhai Space Centre,undefined
[4] Indian Space Research Organization,undefined
[5] Dr. Sivanthi Aditanar College of Engineering,undefined
来源：
Multimedia Tools and Applications | 2023年 / 82卷
关键词：
SPM; SVM; SURF; Feature reduction; Classification;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Schizophrenia affects the substances of the brain, which decreases the volume of the brain and leads to mental disorder. This work deals with the study of using computer aided technique on early diagnose of schizophrenia. Statistical parametric mapping (SPM) is used to separate Gray matter, White matter, and Cerebrospinal fluid from Brain and computed the volume of the brain. We also executed 2D SURF and FAST features and identified the apt feature vector for diagnosing Schizophrenia accurately. Principal Component Analysis is used to find out the most promising feature vectors and SVM Classifier is used to diagnose whether the user was affected by Schizophrenia or not. During the analysis, it was found that FAST feature overperforms the SURF feature in early diagnose of Schizophrenia and results were compared with earlier work.
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页码：32505 / 32517
页数：12
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