Hyperspecral skin imaging with artificial neural networks validated by optical biotissue phantoms

Alexander Bykov (Corresponding author), Evgeny Zherebtsov, Viktor Dremin, Alexey Popov, Alexander Doronin, Igor Meglinski

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


State-of-the-art micro-optic multichannel matrix sensor combined with the tunable Fabry-Perot micro interferometer enables a compact diagnostic device sensitive to the changes of the oxygen saturation as well as the blood volume fraction of human skin. The possibility of using Monte-Carlo modelling for neural network training in the problem of hyperspectral image processing has been demonstrated and validated using biotissue phantom and human skin in vivo. The proposed approach enables a tool combining both the speed of neural network processing and the accuracy and flexibility of Monte-Carlo modelling.
Original languageEnglish
Title of host publicationComputational Optical Sensing and Imaging
Subtitle of host publicationImaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP)
PublisherOptical Society of America OSA
ISBN (Print)978-1-943580-63-7
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventComputational Optical Sensing and Imaging, COSI 2019 - Munich, Germany
Duration: 24 Jun 201927 Jun 2019

Publication series

SeriesOptics InfoBase Conference Papers


ConferenceComputational Optical Sensing and Imaging, COSI 2019


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