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Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics

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Abstract

Excitation-emission matrices (EEM) and total synchronous fluorescence spectra (SFS) of normal and malignant breast tissue specimens are measured in UV–VIS spectral region to serve as data inputs in development of Support Vector Machine (SVM) based breast cancer diagnostics tool. Various input data combinations are tested for classification accuracy using SVM prediction against histopathology findings to discover the best combination regarding diagnostics sensitivity and specificity. It is shown that with EEM data SVM provided 67 % sensitivity and 62 % specificity diagnostics. With SFS data SVM provided 100 % sensitivity and specificity for a several input data combinations. Among these combinations those that require minimal data inputs are identified.

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Acknowledgments

Authors acknowledge the support of the Ministry of Education and Science of the Republic of Serbia (project numbers 45020 and 173049).

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Authors and Affiliations

  1. Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001, Belgrade, Serbia

    Tatjana Dramićanin, Lea Lenhardt, Ivana Zeković & Miroslav D. Dramićanin

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  1. Tatjana Dramićanin
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  2. Lea Lenhardt
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  3. Ivana Zeković
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  4. Miroslav D. Dramićanin
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Correspondence to Miroslav D. Dramićanin.

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Dramićanin, T., Lenhardt, L., Zeković, I. et al. Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics. J Fluoresc 22, 1281–1289 (2012). https://doi.org/10.1007/s10895-012-1070-0

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  • DOI: https://doi.org/10.1007/s10895-012-1070-0

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