Analysis of decision support methods in polarization introscopy systems of biological tissues and fluids

The article discusses the features of the application of decision support methods based on machine learning, fuzzy logic and neural networks in polarization introscopy systems of biological objects. It was determined that methods such as fuzzy logic, some machine learning methods (decision trees, XGBoost) and neural networks (multilayer perceptron) allow to achieve an increase in the accuracy of polarization diagnostics of BS to the level of 81-98%. However, the obtained accuracy results may be overestimated due to the imperfection of the evaluation models and methods of sample formation, which requires further research. A comparative analysis of their accuracy characteristics is presented, taking into account the input data, software implementation and the type of pathologies diagnosed in the introscopy system.