Application of support vector machines to breast cancer screening using mammogram and history data

Walker H. Land Jr.; Anab Akanda; Joseph Y. Lo; Francis Anderson; Margaret Bryden

doi:10.1117/12.467206

9 May 2002 Application of support vector machines to breast cancer screening using mammogram and history data

Walker H. Land Jr., Anab Akanda, Joseph Y. Lo, Francis Anderson, Margaret Bryden

Proceedings Volume 4684, Medical Imaging 2002: Image Processing; (2002) https://doi.org/10.1117/12.467206
Event: Medical Imaging 2002, 2002, San Diego, California, United States

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.

Citation Download Citation

Walker H. Land Jr., Anab Akanda, Joseph Y. Lo, Francis Anderson, and Margaret Bryden "Application of support vector machines to breast cancer screening using mammogram and history data", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); https://doi.org/10.1117/12.467206

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