Development of a neural network model for diagnosing diseases gastrointestinal tract in dogs

Tolstova Elizaveta Antonovna, Ormeli Alexander Fedorovich, Bolshelapov Mikhail Alexandrovich, Selyutin Alexander Dmitrievich

The article provides a solution to the problem of diagnosing diseases of the gastrointestinal tract in dogs. This task is relevant for expert veterinarians. To solve this problem, a neural network model of a multi-layer perceptron was developed, which allows classifying diseases depending on incoming input parameters, and a web interface was created for the trained model. The developed interface allows you to interact with the system in the most comfortable way. Object: development of a multi-layer perceptron neural network model for classification of diseases of the gastrointestinal tract in dogs. Methods: general theoretical - analysis of special literature, study of statistical data; mathematical - network modeling, programming, visualization. Findings: a system for diagnosing diseases of the gastrointestinal tract in dogs was developed. The system has a user-friendly web interface. Conclusions: the developed system can be used by veterinarians when there are difficulties in diagnosing a specific disease of the gastrointestinal tract in dogs.

Keywords:gastritis, pancreatitis, stomach ulcer, Flask, multi-layer perceptron