Automated classification of MRI based on hybrid Least Square Support Vector Machine and Chaotic PSO

Abstract

The objective of this study is to investigate the use of LSSVM (Least Square Support Vector Machine) trained with Chaotic PSO (Particle Swarm Optimization) for distinguishing different levels of Dementia from brain MRI. The availability of an effective method that is more objective than human readers can potentially lead to more reliable and reproducible dementia diagnostic procedures. The proposed scheme consists of several steps including feature extraction, feature selection and classification. This research paper proposes an intelligent classification technique to identify normal and demented patients using LSSVM. The manual interpretation of large volumes of brain MRI may lead to incomplete diagnosis. Hence the LSSVM approach is trained with multiple biomarkers to facilitate effective, accurate classification which is a requirement of the hour. SVM-PSO, LS-SVM-PSO classifiers are compared with LS-SVM trained by Chaotic PSO. LS-SVM-Chaotic PSO yields 100% accurate results and outperforms other classifiers in terms of sensitivity, specificity and accuracy in this analysis.