Diagnosis of dermatophytosis in cats using artificial neural networks

Abstract

The purpose of the research, the results of which are presented in this article, is to determine the possibility and evaluate the effectiveness of using a trained neural network in the diagnosis of ringworm. The article provides an analysis of the methods used for diagnosing dermatomycosis in veterinary practice. One of the actively developing areas at present is the use of artificial neural networks in the diagnosis of animal diseases. The authors have developed a method for diagnosing dermatophytosis using a trained neural network. To identify hair damaged by dermatophyte spores in cats, a trained artificial neural network YOLO v5 was used, based on the YOLO architecture (high-precision artificial neural network), which provides high accuracy and speed of object detection in images. Diagnostics was carried out in three stages. The first stage: the diagnosis of hair in cats damaged by dermatophyte spores was carried out using a trained artificial neural network. The second stage: microscopy by a veterinary specialist of the veterinary center. The third stage: comparison of the received data from the trained artificial neural network and veterinary specialists. Three comparative experiments were carried out on 20 depersonalized samples with different ratios from healthy and sick animals. As a result of testing the trichoscopy method using artificial neural networks for diagnosing spore-damaged hair dermatitis in cats, it was found that a trained artificial neural network of 60 studied samples diagnosed dermatophyte spore damage in 20 samples, a veterinarian - in 17. All positive results were confirmed by a mycological laboratory study. and identification of the pathogen. It has been established that the use of a trained artificial neural network increases the diagnostic efficiency by 15% and reduces the time to perform diagnostic microscopy by 60.3%. The application of the proposed method allows to reduce the time of microscopic examination, improve the accuracy of interpretation of the results, automate methods for identifying causative agents of ringworm in small animals and take timely measures to treat the animal.