Three-class classification in computer-aided diagnosis of breast cancer by support vector machine

Xuejun Sun; Wei Qian; Dansheng Song

doi:10.1117/12.533461

12 May 2004 Three-class classification in computer-aided diagnosis of breast cancer by support vector machine

Xuejun Sun, Wei Qian, Dansheng Song

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.533461
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Design of classifier in computer-aided diagnosis (CAD) scheme of breast cancer plays important role to its overall performance in sensitivity and specificity. Classification of a detected object as malignant lesion, benign lesion, or normal tissue on mammogram is a typical three-class pattern recognition problem. This paper presents a three-class classification approach by using two-stage classifier combined with support vector machine (SVM) learning algorithm for classification of breast cancer on mammograms. The first classification stage is used to detect abnormal areas and normal breast tissues, and the second stage is for classification of malignant or benign in detected abnormal objects. A series of spatial, morphology and texture features have been extracted on detected objects areas. By using genetic algorithm (GA), different feature groups for different stage classification have been investigated. Computerized free-response receiver operating characteristic (FROC) and receiver operating characteristic (ROC) analyses have been employed in different classification stages. Results have shown that obvious performance improvement in both sensitivity and specificity was observed through proposed classification approach compared with conventional two-class classification approaches, indicating its effectiveness in classification of breast cancer on mammograms.

Citation Download Citation

Xuejun Sun, Wei Qian, and Dansheng Song "Three-class classification in computer-aided diagnosis of breast cancer by support vector machine", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.533461

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