Application of convolutional neural networks for recognition of diabetic retinopathy in digital images

Abstract

The aim of the research. Training of a convolutional neural network for recognition of diabetic retinopathy in digital images of the ocular fundus. Material and methods. A set of digital images from the Kaggle open-access digital repository was used. Th e set included 12,498 scans of the ocular fundus: 4,584 scans included signs of diabetic retinopathy while 7,914 images did not have such signs. Th e images with signs of diabetic retinopathy were additionally subdivided according to the disease stages. Building of mathematical models for the convolutional neural network was performed in the PyCharm programming environment using the Python programming language. Results. Th e maximum accuracy achieved during training of the mathematical model of the convolutional neural network for classification into 5 classes (absence of diabetic retinopathy signs and 4 stages in presence of such signs) was only 57.4 % [56.9; 57.9]. Training of the mathematical model of the convolutional neural network for binary classification (presence of retinopathy at any stage or its absence) lead to 80.7 % accuracy [79.9; 81.2] in recognition of diabetic retinopathy on digital images of the eye retina. Conclusion. A trained convolutional neural network model for binary classification provides for a sufficient degree of accuracy in classification and recognition of presence of diabetic retinopathy in digital images of the eye retina. Th erefore, this model may be applied for screening of diabetic retinopathy if integrated with medical information systems or relevant medical equipment.