Machine learning aided fiber-optical system for liver cancer diagnosis in minimally invasive surgical interventions

Zherebtsov, E.; Zajnulina, M.; Kandurova, K.; Dremin, V.; Mamoshin, A.; Potapova, E.; Sokolovski, S.; Dunaev, A.; Rafailov, E.U. (2020-12-15)
 
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https://doi.org/10.1109/ICLO48556.2020.9285445

Zherebtsov, E.
Zajnulina, M.
Kandurova, K.
Dremin, V.
Mamoshin, A.
Potapova, E.
Sokolovski, S.
Dunaev, A.
Rafailov, E.U.
Institute of Electrical and Electronics Engineers
15.12.2020

E. Zherebtsov et al., "Machine Learning aided Fiber-Optical System for Liver Cancer Diagnosis in Minimally Invasive Surgical Interventions," 2020 International Conference Laser Optics (ICLO), St. Petersburg, Russia, 2020, pp. 1-1, doi: 10.1109/ICLO48556.2020.9285445

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doi:https://doi.org/10.1109/ICLO48556.2020.9285445
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https://urn.fi/URN:NBN:fi-fe202103036410
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Abstract

A flexible fibre optical probe is implemented to record the parameters of the endogenous fluorescence during minimally invasive interventions in patients with cancers of hepatoduodenal area. Using machine learning techniques, the obtained spectra are classified to indicate cancerous or healthy tissue. For this, a set of different binary classifiers has been trained and tested. The classifiers showing best performance for this task are identified.

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