Authors: Dramićanin, Tatjana; Dimitrijević, Bogomir; Dramićanin, Miroslav D.

Source: Applied Spectroscopy, Volume 65, Issue 3, Pages 58A-92A and 243-357 (March 2011) , pp. 293-297(5)

Publisher: Society for Applied Spectroscopy

Abstract:

Data from total synchronous fluorescence spectroscopy (TSFS) measurements of normal and malignant breast tissue samples are introduced in supervised self-organizing maps, a type of artificial neural network (ANN), to obtain diagnosis. Three spectral regions in both TSFS patterns and first-derivative TSFS patterns exhibited clear differences between normal and malignant tissue groups, and intensities measured from these regions served as inputs to neural networks. Histology findings are used as the gold standard to train self-organizing maps in a supervised way. Diagnostic accuracy of this procedure is evaluated with sample test groups for two cases, when the neural network uses TSFS data and when the neural network uses data from first-derivative TSFS. In the first case diagnostic sensitivity of 87.1% and specificity of 91.7% are found, while in the second case sensitivity of 100% and specificity of 94.4% are achieved.

Keywords: TOTAL SYNCHRONOUS FLUORESCENCE SPECTROSCOPY; BREAST CANCER; ARTIFICIAL NEURAL NETWORKS; ANNS; SELF-ORGANIZING MAPS; SOMS

Document Type: Research article

DOI: http://dx.doi.org/10.1366/10-05928

Affiliations: 1: Institute of Nuclear Sciences "Vinĉa," University of Belgrade, P.O. Box 522, 11001 Belgrade, Serbia

Publication date: 2011-03-01

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