DETECTION OF THE SIGNS OF DIABETIC RETINOPATHY USING CLASSIFICATION MATHEMATICAL MODELS
The research purpose was to develop mathematical models for the detection of digital images of the retina to diagnose the diabetic retinopathy. Materials and methods. The study used images from the open repository Standard Diabetic Retinopathy Database. All images used in the study were marked up by experts into four patterns of diabetic retinopathy - soft exudates, hard exudates, hemorrhages and microaneurysms. The image data has been pre - parameterized for convenient use of all parameters as input data for classification mathematical models. During parameterization, 35 parameters were identified that describe 3x3 pixel image sections. The following classification mathematical models were used to classify pixels in images: discriminant equation, classification tree, logistic regression equation, and artificial multilayer neural network. Results and its discussion. As a result of constructing these models and comparing the results of pixel classification, it was found that the highest classification accuracy is achieved by using the classification tree model. For the search for hard exudates in retinal images, the classification accuracy index is 81.3 [81.1; 81.5]%, for hemorrhages 79.0 [78.1; 79.9]%, for microaneurysms 84.1 [83.2; 84.8]%, for soft exudates 89.6 [88.8; 90.3]%. Conclusion. The use of these models allows to carry out an automatic analysis of digital images of the retina and search for pixels related to the signs of diabetic retinopathy, the totality of which indicates the presence or absence of this complication of diabetes mellitus.