18 January 2005 Detecting microcalcifications in mammograms by using SVM method for the diagnostics of breast cancer
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Abstract
Support vector machine (SVM) is a new statistical learning method. Compared with the classical machine learning methods, SVM learning discipline is to minimize the structural risk instead of the empirical risk of the classical methods, and it gives better generative performance. Because SVM algorithm is a convex quadratic optimization problem, the local optimal solution is certainly the global optimal one. In this paper a SVM algorithm is applied to detect the micro-calcifications (MCCs) in mammograms for the diagnostics of breast cancer that has not been reported yet. It had been tested with 10 mammograms and the results show that the algorithm can achieve a higher true positive in comparison with artificial neural network (ANN) based on the empirical risk minimization, and is valuable for further study and application in the clinical engineering.
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Baikun Wan, Ruiping Wang, Hongzhi Qi, Xuchen Cao, "Detecting microcalcifications in mammograms by using SVM method for the diagnostics of breast cancer", Proc. SPIE 5630, Optics in Health Care and Biomedical Optics: Diagnostics and Treatment II, (18 January 2005); doi: 10.1117/12.569618; https://doi.org/10.1117/12.569618
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