TY  - JOUR
AU  - Hut, Igor
AU  - Jeftić, Branislava
AU  - Matija, Lidija
AU  - Ćojbašić, Žarko
AU  - Koruga, Đuro
PY  - 2019
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3004
AB  - Semi-automated system for classification of cervical smear images based on Optomagnetic Imaging Spectroscopy (OMIS) and machine learning is proposed. Optomagnetic Imaging Spectroscopy has been applied to screen 700 cervical samples prepared according to Liquid Based Cytology (LBC) principles and to record spectra of the samples. Peak intensities and peak shift frequencies from the spectra have been used as features in classification models. Several machine learning algorithms have been tested and results of classification have been compared. Results suggest that the presented approach can be used to improve standard LBC screening tests for cervical cancer detection. Developed system enables detection of pre-cancerous and cancerous states with sensitivity of 79% and specificity of 83% along with AUC (ROC) of 88% and could be used as an improved alternative procedure for cervical cancer screening. Moreover, this can be achieved via portable apparatus and with immediately available results.
PB  - Univ Osijek, Tech Fac, Slavonski Brod
T2  - Tehnički vjesnik
T1  - Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy
EP  - 1699
IS  - 6
SP  - 1694
VL  - 26
DO  - 10.17559/TV-20190528192618
ER  - 

@article{
author = "Hut, Igor and Jeftić, Branislava and Matija, Lidija and Ćojbašić, Žarko and Koruga, Đuro",
year = "2019",
abstract = "Semi-automated system for classification of cervical smear images based on Optomagnetic Imaging Spectroscopy (OMIS) and machine learning is proposed. Optomagnetic Imaging Spectroscopy has been applied to screen 700 cervical samples prepared according to Liquid Based Cytology (LBC) principles and to record spectra of the samples. Peak intensities and peak shift frequencies from the spectra have been used as features in classification models. Several machine learning algorithms have been tested and results of classification have been compared. Results suggest that the presented approach can be used to improve standard LBC screening tests for cervical cancer detection. Developed system enables detection of pre-cancerous and cancerous states with sensitivity of 79% and specificity of 83% along with AUC (ROC) of 88% and could be used as an improved alternative procedure for cervical cancer screening. Moreover, this can be achieved via portable apparatus and with immediately available results.",
publisher = "Univ Osijek, Tech Fac, Slavonski Brod",
journal = "Tehnički vjesnik",
title = "Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy",
pages = "1699-1694",
number = "6",
volume = "26",
doi = "10.17559/TV-20190528192618"
}

Hut, I., Jeftić, B., Matija, L., Ćojbašić, Ž.,& Koruga, Đ.. (2019). Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy. in Tehnički vjesnik
Univ Osijek, Tech Fac, Slavonski Brod., 26(6), 1694-1699.
https://doi.org/10.17559/TV-20190528192618

Hut I, Jeftić B, Matija L, Ćojbašić Ž, Koruga Đ. Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy. in Tehnički vjesnik. 2019;26(6):1694-1699.
doi:10.17559/TV-20190528192618 .

Hut, Igor, Jeftić, Branislava, Matija, Lidija, Ćojbašić, Žarko, Koruga, Đuro, "Machine Learning Classification of Cervical Tissue Liquid Based Cytology Smear Images by Optomagnetic Imaging Spectroscopy" in Tehnički vjesnik, 26, no. 6 (2019):1694-1699,
https://doi.org/10.17559/TV-20190528192618 . .