Hyperspecral skin imaging with artificial neural networks validated by optical biotissue phantoms
Bykov, A.; Zherebtsov, E.; Dremin, V.; Popov, A.; Doronin, A.; Meglinski, I. (2019-06-24)
A. Bykov, E. Zherebtsov, V. Dremin, A. Popov, A. Doronin, and I. Meglinski, "Hyperspecral Skin Imaging with Artificial Neural Networks Validated by Optical Biotissue Phantoms," in Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP), OSA Technical Digest (Optical Society of America, 2019), paper CW1A.3., DOI: https://doi.org/10.1364/COSI.2019.CW1A.3
© 2019 The Author(s). This is the Authors Accepted Manuscript version of this article published by The Optical Society of America. The Definitive Version of Record can be found online at https://doi.org/10.1364/COSI.2019.CW1A.3.
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.
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