A support vector machine based classifier to extract abnormal features from breast magnetic resonance images

Authors:
Jucheol Moon;Hyun I. Kim;Hyung D. Choi;Soon I. Jeon
Affiliations:
South Dakota State University, Brookings, SD;South Dakota State University, Brookings, SD;Electronics and Telecommunications Research Institute (ETRI), DaeJeon, South Korea;Electronics and Telecommunications Research Institute (ETRI), DaeJeon, South Korea
Venue:
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Year:
2012

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Abstract

Magnetic resonance imaging (MRI) is one of the high quality technologies to detect the breast cancer. This study proposes a new framework to extract abnormal features in Magnetic Resonance (MR) images by concentrating on the key aspect of the features: generating a unique input sequence to apply the Support Vector Machine (SVM) classifier. The main contribution of the proposed approach is the improvement of an accuracy in identifying abnormal features using SVM classifier. This approach is also less sensitive to noise in detecting the breast cancer. In order to evaluate the improved performance of the proposed SVM classifier, the results of traditional Decision Tree (DT) classifier has been compared with that of SVM.