Application of shallow and deep convolutional neural networks to recognize the average flow rate of physiological fluids in a capillary

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%.