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Proceedings Paper

Application of support vector machines to breast cancer screening using mammogram and history data
Author(s): Walker H. Land Jr.; Anab Akanda; Joseph Y. Lo; Francis Anderson; Margaret Bryden
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Paper Abstract

Support Vector Machines (SVMs) are a new and radically different type of classifiers and learning machines that use a hypothesis space of linear functions in a high dimensional feature space. This relatively new paradigm, based on Statistical Learning Theory (SLT) and Structural Risk Minimization (SRM), has many advantages when compared to traditional neural networks, which are based on Empirical Risk Minimization (ERM). Unlike neural networks, SVM training always finds a global minimum. Furthermore, SVMs have inherent ability to solve pattern classification without incorporating any problem-domain knowledge. In this study, the SVM was employed as a pattern classifier, operating on mammography data used for breast cancer detection. The main focus was to formulate the best learning machine configurations for optimum specificity and positive predictive value at very high sensitivities. Using a mammogram database of 500 biopsy-proven samples, the best performing SVM, on average, was able to achieve (under statistical 5-fold cross-validation) a specificity of 45.0% and a positive predictive value (PPV) of 50.1% at 100% sensitivity. At 97% sensitivity, a specificity of 55.8% and a PPV of 55.2% were obtained.

Paper Details

Date Published: 9 May 2002
PDF: 7 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467206
Show Author Affiliations
Walker H. Land Jr., Univ. of Binghamton (United States)
Anab Akanda, Univ. of Binghamton (United States)
Joseph Y. Lo, Duke Univ. Medical Ctr. (United States)
Francis Anderson, Our Lady of Lourdes Memorial Hospital (United States)
Margaret Bryden, Univ. of Binghamton (United States)

Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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