Hyperspectral imaging of human skin aided by artificial neural networks

Evgeny Zherebtsov, Viktor Dremin, Alexey Popov, Alexander Doronin, Daria Kurakina, Mikhail Kirillin, Igor Meglinski, Alexander Bykov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We developed a compact, hand-held hyperspectral imaging system for 2D neural network-based visualization of skin chromophores and blood oxygenation. State-of-the-art micro-optic multichannel matrix sensor combined with the tunable Fabry-Perot micro interferometer enables a portable diagnostic device sensitive to the changes of the oxygen saturation as well as the variations of blood volume fraction of human skin. Generalized object-oriented Monte Carlo model is used extensively for the training of an artificial neural network utilized for the hyperspectral image processing. In addition, the results are verified and validated via actual experiments with tissue phantoms and human skin in vivo. The proposed approach enables a tool combining both the speed of an artificial neural network processing and the accuracy and flexibility of advanced Monte Carlo modeling. Finally, the results of the feasibility studies and the experimental tests on biotissue phantoms and healthy volunteers are presented.

Original languageEnglish
Article number364396
Pages (from-to)3545-3559
Number of pages15
JournalBiomedical Optics Express
Issue number7
Publication statusPublished - 24 Jun 2019

Bibliographical note

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Funding: Academy of Finland (290596, 314369, 318281); Ministry of Science and Higher Education of Russian Federation (0035-2019-0014)


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