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

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 freeresponse 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 twoclass classification approaches, indicating its effectiveness in classification of breast cancer on mammograms.