Features of the Modification of the Inceptionresnetv2 Architecture and the Creation of a Diagnostic System for Determining the Degree of Damage to Retinal Vessels

Abstract

Diabetic retinopathy is a retinal disease caused by diabetes. The progression of this disease can lead to blindness. Every year, the number of patients with this disease increases. Diabetic retinal damage can be slowed if it is diagnosed early. The article describes the features of the creation of a neural network model and the development of a system with high accuracy rates for the recognition of diabetic retinopathy. The advantages of the InceptionResNetv2 convolutional neural network architecture are considered. This network uses residual connections that help facilitate the learning process. InceptionResNetv2 uses different methods to reduce the dimensionality of the feature map, making it more economical in terms of memory and computation. This model has a number of advantages compared to other networks. InceptionResNetv2 can be applied to blood vessel segmentation in eye images with different resolutions. In the study, modification of InceptionResNetv2 was carried out. The use of additional MaxPooling and Dense layers improved the speed and accuracy of the InceptionResNetv2 convolutional neural network. The Dropout layer is effectively used to prevent overtraining. The system for determining the degree of retinal damage of diabetic origin is implemented in the Python programming language. Model layers are built using the Keras library. Images from the set of EyePacs were processed by methods of cropping the black frames with a Gaussian blur filter and minimizing the effect of changing the position of the images. During the research, it was found that 21 epochs are needed to achieve maximum accuracy. The program calculates the probability of an image belonging to a certain class with high accuracy. The recognition accuracy rate for class 1 was 98.6%, for class 2 - 98.5%, for class 3 - 98.3%, for class 4 - 98.15%, for class 5 - 98.1%.