Deep learning approach for retinopathy identification in combined clinical datasets

This work presents a system for automatic detection of various stages of diabetic retinopathy (DR) based on fundus images of patients. The system was built based on a relatively new and little-used image database: ”Dataset of fundus images for the study of diabetic retinopathy” version v3 CastilloBenitez21. The primary dataset was expanded using clinical fundus photographs acquired from the Department of Nephrology at Wroclaw Medical University. The diagnostic system was developed based on various variants of convolutional neural networks (CNNs) that were pre-trained on ImageNet data. The CNN classifier, based on VGG16 with transfer learning, proved to be effective and gave a global accuracy of 83.15%. The evaluation of discrimination between the non-DR and the DR state resulted in an accuracy of 89.7%, with a sensitivity of 94.9%, a specificity of 88.3%, and a Matthews Correlation Coefficient of 0.7665.