Stebakov, Ivan, Kornaeva, Elena, Potapova, Elena and Dremin, Viktor (2022). Application of shallow and deep convolutional neural networks to recognize the average flow rate of physiological fluids in a capillary. Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 12194 ,
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%.
| Publication DOI: | https://doi.org/10.1117/12.2626125 |
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| Divisions: | College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT) |
| Funding Information: | This work was supported by the Russian Science Foundation under the Project acknowledge this support. This work was supported by the Russian Science Foundation under the Project 20-79-00332. The authors gratefully acknowledge this support. |
| Additional Information: | 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 |
| Uncontrolled Keywords: | artificial neural network,flow rate,laser speckle contrast imaging,physiological fluid,rheology,Electronic, Optical and Magnetic Materials,Atomic and Molecular Physics, and Optics,Biomaterials,Radiology Nuclear Medicine and imaging |
| Publication ISSN: | 1605-7422 |
| Last Modified: | 16 Dec 2024 08:39 |
| Date Deposited: | 29 Jun 2022 10:53 |
| Full Text Link: | |
| Related URLs: |
http://www.scop ... tnerID=8YFLogxK
(Scopus URL) https://www.spi ... 12.2626125.full (Publisher URL) |
PURE Output Type: | Conference article |
| Published Date: | 2022-04-29 |
| Accepted Date: | 2021-09-01 |
| Authors: |
Stebakov, Ivan
Kornaeva, Elena Potapova, Elena Dremin, Viktor ( 
0000-0001-6974-3505)
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CORE (COnnecting REpositories)
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