TY  - JOUR
AU  - Dramićanin, Tatjana
AU  - Dimitrijević, Bogomir B.
AU  - Dramićanin, Miroslav
PY  - 2011
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/4235
AB  - 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.
T2  - Applied Spectroscopy
T1  - Application of Supervised Self-Organizing Maps in Breast Cancer Diagnosis by Total Synchronous Fluorescence Spectroscopy
VL  - 65
IS  - 3
SP  - 293
EP  - 297
DO  - 10.1366/10-05928
ER  - 

@article{
author = "Dramićanin, Tatjana and Dimitrijević, Bogomir B. and Dramićanin, Miroslav",
year = "2011",
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.",
journal = "Applied Spectroscopy",
title = "Application of Supervised Self-Organizing Maps in Breast Cancer Diagnosis by Total Synchronous Fluorescence Spectroscopy",
volume = "65",
number = "3",
pages = "293-297",
doi = "10.1366/10-05928"
}

Dramićanin, T., Dimitrijević, B. B.,& Dramićanin, M.. (2011). Application of Supervised Self-Organizing Maps in Breast Cancer Diagnosis by Total Synchronous Fluorescence Spectroscopy. in Applied Spectroscopy, 65(3), 293-297.
https://doi.org/10.1366/10-05928

Dramićanin T, Dimitrijević BB, Dramićanin M. Application of Supervised Self-Organizing Maps in Breast Cancer Diagnosis by Total Synchronous Fluorescence Spectroscopy. in Applied Spectroscopy. 2011;65(3):293-297.
doi:10.1366/10-05928 .

Dramićanin, Tatjana, Dimitrijević, Bogomir B., Dramićanin, Miroslav, "Application of Supervised Self-Organizing Maps in Breast Cancer Diagnosis by Total Synchronous Fluorescence Spectroscopy" in Applied Spectroscopy, 65, no. 3 (2011):293-297,
https://doi.org/10.1366/10-05928 . .