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Proceedings Paper

Classification of masses on mammograms using support vector machine
Author(s): Yong Chu; Lihua Li; Dmitry B. Goldgof; Yan Qui; Robert A. Clark M.D.
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Paper Abstract

Mammography is the most effective method for early detection of breast cancer. However, the positive predictive value for classification of malignant and benign lesion from mammographic images is not very high. Clinical studies have shown that most biopsies for cancer are very low, between 15% and 30%. It is important to increase the diagnostic accuracy by improving the positive predictive value to reduce the number of unnecessary biopsies. In this paper, a new classification method was proposed to distinguish malignant from benign masses in mammography by Support Vector Machine (SVM) method. Thirteen features were selected based on receiver operating characteristic (ROC) analysis of classification using individual feature. These features include four shape features, two gradient features and seven Laws features. With these features, SVM was used to classify the masses into two categories, benign and malignant, in which a Gaussian kernel and sequential minimal optimization learning technique are performed. The data set used in this study consists of 193 cases, in which there are 96 benign cases and 97 malignant cases. The leave-one-out evaluation of SVM classifier was taken. The results show that the positive predict value of the presented method is 81.6% with the sensitivity of 83.7% and the false-positive rate of 30.2%. It demonstrated that the SVM-based classifier is effective in mass classification.

Paper Details

Date Published: 15 May 2003
PDF: 9 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481142
Show Author Affiliations
Yong Chu, Univ. of South Florida (United States)
Lihua Li, Univ. of South Florida (United States)
Dmitry B. Goldgof, Univ. of South Florida (United States)
Yan Qui, Univ. of South Florida (United States)
Robert A. Clark M.D., Univ. of South Florida (United States)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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