Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics
Apstrakt
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.
Ključne reči:
Breast cancer / Fluorescence / Support vector machine / Synchronous fluorescenceIzvor:
Journal of Fluorescence, 2012, 22, 5, 1281-1289Finansiranje / projekti:
- Materijali redukovane dimenzionalnosti za efikasnu apsorpciju svetlosti i konverziju energije (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-45020)
- Molekularne determinante za dizajn tumor markera (RS-MESTD-Basic Research (BR or ON)-173049)
DOI: 10.1007/s10895-012-1070-0
ISSN: 1053-0509
PubMed: 22678149
WoS: 000308064100011
Scopus: 2-s2.0-84869082830
Kolekcije
Institucija/grupa
VinčaTY - JOUR AU - Dramićanin, Tatjana AU - Lenhardt, Lea I. AU - Zeković, Ivana Lj. AU - Dramićanin, Miroslav PY - 2012 UR - https://vinar.vin.bg.ac.rs/handle/123456789/5020 AB - 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. T2 - Journal of Fluorescence T1 - Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics VL - 22 IS - 5 SP - 1281 EP - 1289 DO - 10.1007/s10895-012-1070-0 ER -
@article{ author = "Dramićanin, Tatjana and Lenhardt, Lea I. and Zeković, Ivana Lj. and Dramićanin, Miroslav", year = "2012", 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.", journal = "Journal of Fluorescence", title = "Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics", volume = "22", number = "5", pages = "1281-1289", doi = "10.1007/s10895-012-1070-0" }
Dramićanin, T., Lenhardt, L. I., Zeković, I. Lj.,& Dramićanin, M.. (2012). Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics. in Journal of Fluorescence, 22(5), 1281-1289. https://doi.org/10.1007/s10895-012-1070-0
Dramićanin T, Lenhardt LI, Zeković IL, Dramićanin M. Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics. in Journal of Fluorescence. 2012;22(5):1281-1289. doi:10.1007/s10895-012-1070-0 .
Dramićanin, Tatjana, Lenhardt, Lea I., Zeković, Ivana Lj., Dramićanin, Miroslav, "Support Vector Machine on Fluorescence Landscapes for Breast Cancer Diagnostics" in Journal of Fluorescence, 22, no. 5 (2012):1281-1289, https://doi.org/10.1007/s10895-012-1070-0 . .