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

A. Bykov; E. Zherebtsov; V. Dremin; A. Popov; A. Doronin; I. Meglinski

doi:10.1364/COSI.2019.CM1A.CW1A.3

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

A. Bykov^*, E. Zherebtsov, V. Dremin, A. Popov, A. Doronin, I. Meglinski

^*Corresponding author for this work

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

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 language	English
Title of host publication	Computational Optical Sensing and Imaging, COSI 2019
ISBN (Electronic)	9781557528209
DOIs	https://doi.org/10.1364/COSI.2019.CM1A.CW1A.3
Publication status	Published - 27 Jun 2019
Event	Computational Optical Sensing and Imaging, COSI 2019 - Munich, Germany Duration: 24 Jun 2019 → 27 Jun 2019

Conference

Conference	Computational Optical Sensing and Imaging, COSI 2019
Country/Territory	Germany
City	Munich
Period	24/06/19 → 27/06/19

Bibliographical note

Funding Information:
Authors acknowledge the support of the Academy of Finland (grants: 290596, 296408).

Publisher Copyright:
© OSA 2019 © 2019 The Author(s)

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Access to Document

10.1364/COSI.2019.CM1A.CW1A.3

Cite this

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title = "Hyperspecral skin imaging with artificial neural networks validated by optical biotissue phantoms",

abstract = "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.",

author = "A. Bykov and E. Zherebtsov and V. Dremin and A. Popov and A. Doronin and I. Meglinski",

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AU - Bykov, A.

AU - Zherebtsov, E.

AU - Dremin, V.

AU - Popov, A.

AU - Doronin, A.

AU - Meglinski, I.

N1 - Funding Information: Authors acknowledge the support of the Academy of Finland (grants: 290596, 296408). Publisher Copyright: © OSA 2019 © 2019 The Author(s) Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2019/6/27

Y1 - 2019/6/27

N2 - 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.

AB - 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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U2 - 10.1364/COSI.2019.CM1A.CW1A.3

DO - 10.1364/COSI.2019.CM1A.CW1A.3

M3 - Conference publication

AN - SCOPUS:85089887089

BT - Computational Optical Sensing and Imaging, COSI 2019

T2 - Computational Optical Sensing and Imaging, COSI 2019

Y2 - 24 June 2019 through 27 June 2019

ER -

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

Abstract

Conference

Bibliographical note

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