An Improved SVM Classifier for Medical Image Classification

Authors:
Yun Jiang;Zhanhuai Li;Longbo Zhang;Peng Sun
Affiliations:
College of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, P.R. China and College of Mathematics and Information Science, Northwest Normal University, 730070, Lanzhou, P.R. ...;College of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, P.R. China;College of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, P.R. China and School of Computer Science, Shandong University of Technology, Zibo 255049, China;College of Computer Science, Northwestern Polytechnical University, 710072, Xi'an, P.R. China
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

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Abstract

Support Vector Machine (SVM) has high classifying accuracy and good capabilities of fault-tolerance and generalization. The Rough Set Theory (RST) approach has the advantages on dealing with a large amount of data and eliminating redundant information. In this paper, we join SVM classifier with RST which we call the Improved Support Vector Machine (ISVM) to classify digital mammography. The experimental results show that this ISVM classifier can get 96.56% accuracy which is higher about 3.42% than 92.94% using SVM, and the error recognition rates are close to 100% averagely.