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Normal mammogram classification based on a support vector machine utilizing crossed distribution features

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6 Author(s)
W. Chiracharit ; Dept. of Electron. & Telecom. Eng., King Mongkut's Univ. of Technol., Bangkok, Thailand ; Y. Sun ; P. Kumhom ; K. Chamnongthai
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Automatic classification of normal mammograms, which constitute a majority of screening mammograms, is a new approach to computer-aided diagnosis of breast cancer. This approach may be limited, however, by non-separable "crossed" distributions of features that are extracted from digitized mammograms. This work presents a method of mapping such non-separable input features into a new set of separable features that can be utilized, together with ordinary "uncrossed" features, by a support vector machine (SVM) classifier. The results of the proposed scheme show improved performance with 80% sensitivity and 95% specificity.

Published in:

Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE  (Volume:1 )

Date of Conference:

1-5 Sept. 2004