University of Oulu

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

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

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Author: Zherebtsov, E.1,2; Zajnulina, M.3; Kandurova, K.2;
Organizations: 1University of Oulu, Optoelectronics and Measurement Techniques Unit, Oulu, Finland
2Research and Development Center of Biomedical Photonics, Orel State University, Orel, Russia
3Aston Institute of Photonic Technologies, Aston University, Birmingham, UK
4Orel Regional Clinical Hospital, 302028 Orel, Russia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202103036410
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-03-03
Description:

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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Series: International conference laser optics
ISSN: 2640-8201
ISSN-E: 2642-5580
ISSN-L: 2640-8201
ISBN: 978-1-7281-5233-2
ISBN Print: 978-1-7281-5232-5
Pages: 1 - 1
DOI: 10.1109/ICLO48556.2020.9285445
OADOI: https://oadoi.org/10.1109/ICLO48556.2020.9285445
Host publication: 2020 International Conference Laser Optics (ICLO)
Conference: International Conference Laser Optics
Type of Publication: A4 Article in conference proceedings
Field of Science: 217 Medical engineering
Subjects:
Funding: This research was funded by the Russian Science Foundation under grant number 18-15-00201 and MSCA-IF-2017 scheme (ID: 792421).
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