Нові розв’язки відтворення структури розсіювальних середовищ з використанням технології машинного навчання

Editorial Board PCSS Journal; Claudia Zenkova; Dmytro Ivanskyi; Oleg Angelsky; Pavlo Ryabyi; Artur Koniakhin; Mykhailo Diachenko; Xinzheng Zhang

doi:10.15330/pcss.27.3.481-486

Authors

Claudia Zenkova Yuriy Fedkovich Chernivtsi National University, Chernivtsi, Ukraine
Dmytro Ivanskyi Yuriy Fedkovich Chernivtsi National University, Chernivtsi, Ukraine
Oleg Angelsky Yuriy Fedkovich Chernivtsi National University, Chernivtsi, Ukraine
Pavlo Ryabyi Yuriy Fedkovich Chernivtsi National University, Chernivtsi, Ukraine
Artur Koniakhin Yuriy Fedkovich Chernivtsi National University, Chernivtsi, Ukraine
Mykhailo Diachenko Municipal Enterprise Chernivtsi Regional Clinical Cardiological Center, Chernivtsi, Ukraine
Xinzheng Zhang The MOE Key Laboratory of Weak-Light Nonlinear Photonics and International Sino-Slovenian Join Research Center on Liquid Crystal Photonics, TEDA Institute of Applied Physics and School of Physics, Nankai University, Tianjin, China

DOI:

https://doi.org/10.15330/pcss.27.3.481-486

Keywords:

neural network, machine learning, Monte Carlo, amplitude (phase) function, OCT

Abstract

The work is devoted to advancing the idea of using machine learning technology combined with the algorithmic Monte Carlo method to reconstruct the spatial location of scattering centers in the corneal tissue, as well as the density distribution of these centers within the volume during longitudinal scanning of the sample. A favorable prediction is observed for reproducing the sample thickness along with determining the location of each scattering center. The Monte Carlo generated “heat map” of the probability of photon exit coordinates from the investigated medium is used as a set of input data for training the neural network. The proposed convolutional neural network architecture made it possible to achieve up to 98% accuracy in reconstructing the spatial location of scattering centers and the density distribution of these centers during longitudinal scanning.

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