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

Content Provider	IEEE Xplore Digital Library
Author	Sivapriya, T.R. Kamal, A.R.N.B. Thavavel, V.
Copyright Year	2012
Description	Author affiliation: Dept. of Comput. Sci., Lady Doak Coll., Madurai, India (Sivapriya, T.R.; Kamal, A.R.N.B.; Thavavel, V.)
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.
Sponsorship	IEEE Comput. Soc.
Starting Page	1
Ending Page	7
File Size	473827
Page Count	7
File Format	PDF
DOI	10.1109/ICCCNT.2012.6396019
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-26
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Neural Network Chaotic PSO LSSVM Abstracts Programming Support vector machine Reliability PSO Equations Biomedical imaging Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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