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Proceedings Paper

Three-class classification in computer-aided diagnosis of breast cancer by support vector machine
Author(s): Xuejun Sun; Wei Qian; Dansheng Song
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Paper 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.

Paper Details

Date Published: 12 May 2004
PDF: 9 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.533461
Show Author Affiliations
Xuejun Sun, Univ. of South Florida (United States)
Wei Qian, Univ. of South Florida (United States)
Dansheng Song, Univ. of South Florida (United States)

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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