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Proceedings Paper

Breast cancer diagnosis from fluorescence spectroscopy using support vector machine
Author(s): Jiyoung Choi; Sharad Gupta; Inho Park; Doheon Lee; Jong Chul Ye
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Paper Abstract

A novel support vector machine (SVM) classifier incorporating the complexity of fluorescent spectral data is designed to reliably differentiate normal and malignant human breast cancer tissues. Analysis has been carried out with parallel and perpendicularly polarized fluorescence data using 36 normal and 36 cancerous tissue samples. In order to incorporate the complexity of fluorescence spectral profile into a SVM design, the curvature of phase space trajectory is extracted as a useful complexity feature. We found that the fluorescence intensity peaks at 541nm-620nm as well as the complexity features at 621nm-700nm are important discriminating features. By incorporating both features in SVM design, we can improve both sensitivity and specificity of the classifier.

Paper Details

Date Published: 13 February 2007
PDF: 10 pages
Proc. SPIE 6434, Optical Tomography and Spectroscopy of Tissue VII, 64340P (13 February 2007); doi: 10.1117/12.700800
Show Author Affiliations
Jiyoung Choi, Korea Advanced Institute of Science and Technology (South Korea)
Sharad Gupta, Korea Advanced Institute of Science and Technology (South Korea)
Inho Park, Korea Advanced Institute of Science and Technology (South Korea)
Doheon Lee, Korea Advanced Institute of Science and Technology (South Korea)
Jong Chul Ye, Korea Advanced Institute of Science and Technology (South Korea)


Published in SPIE Proceedings Vol. 6434:
Optical Tomography and Spectroscopy of Tissue VII
Britton Chance; Robert R. Alfano; Bruce J. Tromberg; Mamoru Tamura; Eva Marie Sevick-Muraca, Editor(s)

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