Abstract
The aim of this work is to develop practical tools to recognize the average flow rate of physiological fluids in capillaries. This tool is represented by classification models in an artificial neural networks form. The flow rate data were obtained experimentally. Intralipid was used as the test liquid. Laser speckle contrast imaging was used to obtain images of liquid flow in a glass capillary. The experiment was carried out with an average flow rate of 0-2 mm/s with various concentrations of intralipid. The results of training of fully connected and convolutional neural networks for processing the received data are presented. The accuracy of determining the average flow rate of intralipid with different concentrations was comparable to the previously obtained results for a fixed concentration and amounted to approximately 97.5%.
| Original language | English |
|---|---|
| Article number | 121940D |
| Journal | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
| Volume | 12194 |
| DOIs | |
| Publication status | Published - 29 Apr 2022 |
| Event | Saratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics - Saratov, Russian Federation Duration: 27 Sept 2021 → 1 Oct 2021 |
Bibliographical note
Copyright 2022 SPIE. One print or electronic copy may be made for personal use only. Systematic reproduction, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Funding Information:
This work was supported by the Russian Science Foundation
Keywords
- artificial neural network
- flow rate
- laser speckle contrast imaging
- physiological fluid
- rheology