In this paper, we search for optimal installation parameters for measuring the thickness of biological tissues. We created the mathematical model of the meter and researched various configurations of the constituent parts of the model. We also proposed the methods for determining the optimal parameters and we found numerical values the most important cases. We considered different options for modifying the laser triangulation method and proposed a scheme of the measurement setup. We conducted experiments and represented the results obtained with computer processing too.
The purpose of the work: to create an algorithm and implement it in a software tool for classifying photographic images of pathology of the central region of the human fundus, detected by autofluorescence research, according to 8 types-patterns: normal, minimal changes, focal, spotted, linear, lace-like, reticular, speckled. Methods: machine learning algorithms (convolutional neural networks) and computer vision (histogram methods, perceptual hash algorithms). The main feature of the task: an ultra-small set of unique photoimages with an accurately diagnosed type of pathology (18 pieces). The accuracy of forecasts when solving a problem using a neural network is 12.5%. The accuracy of the predictions of the developed algorithm using a combination of histograms, perceptual hash and 1 reference photo of the normal state of the fundus is 60% when selecting the classifier parameters from a set of 1 photo for 1 pathology. When using 3 reference photos, the norm is 85%. The proposed solution can be used in medicine, ophthalmology, photonics and optics of biological tissues, machine learning for both research and educational purposes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.