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2002
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Support vector machine learning for detection of microcalcifications in mammograms

11 years 5 months ago
Support vector machine learning for detection of microcalcifications in mammograms
Microcalcification (MC) clusters in mammograms can be an indicator of breast cancer. In this work we propose for the first time the use of support vector machine (SVM) learning for automated detection of MCs in digitized mammograms. In the proposed framework, MC detection is formulated as a supervised-learning problem and the method of SVM is employed to develop the detection algorithm. The proposed method is developed and evaluated using a database of 76 mammograms containing 1120 MCs. To evaluate detection performance, free-response receiver operating characteristic (FROC) curves are used. Experimental results demonstrate that, when compared to several other existing methods, the proposed SVM framework offers the best performance.
Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Niko
Added 20 Nov 2009
Updated 20 Nov 2009
Type Conference
Year 2002
Where ISBI
Authors Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Nikolas P. Galatsanos, Robert M. Nishikawa
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