Application of artificial neural networks to recognize the average flow rate of physiological fluids in a capillary
Ivan Stebakov,1 Elena Kornaeva,2 Elena Potapova,3 Viktor Dremin,3,4
1 Department of Mechatronics, Mechanics and Robotics, Orel State University named after I.S. Turgenev, Orel, 302026, Russian Federation
2 Department of Information Systems and Digital Technologies, Orel State University named after I.S. Turgenev, Orel, 302026, Russian Federation
3 Research and Development Center of Biomedical Photonics, Orel State University named after I.S. Turgenev, Orel, 302026, Russian Federation
4 College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET, United Kingdom
Abstract
The aim of this work is practical tool development for the recognition of 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 the average flow rate 0..2 mm/s with various concentrations of intralipid. The results of training 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 about 88%.
File with abstract
Speaker
Ivan Stebakov
Orel State University named after I.S. Turgenev
Russian Federation
Discussion
Ask question
