Diabetic retinopathy detection with artificial intelligence

Diabetic retinopathy (DR) is one of the most common and serious eye diseases caused by diabetes, leading to microvascular abnormalities in the retinal blood vessels such as microaneurysms, hemorrhages and exudates. Early diagnosis of DR is of great importance in the treatment process, and given the difficulties of manual diagnostic methods, the development of computer-aided diagnostic systems has become very important. In this study, we evaluate the performance of different deep learning architectures for automatic image classification and lesion detection in DR diagnosis. First, fundus images were subjected to DR classification with VGG16, VGG19, CNN, Xception, InceptionV3 and ResNet50 models without preprocessing. Then, the images were preprocessed and enhanced with the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm and DR classification was performed again and all results were compared. It was observed that CLAHE, which was used as an image preprocessing method, contributed to making small lesions more visible and significantly improved the classification performance. Among the results obtained, VGG16-CLAHE and CNN-CLAHE models were found to provide the highest performance with 99% accuracy. In addition, the VGG16-CLAHE-based deep learning model developed in this study was integrated into a user-friendly interface application, enabling real-time DR diagnosis. The application can successfully distinguish five classes of DR (No DR, Mild NPDR, Moderate NPDR, Severe NPDR, PDR). It is envisaged that system performance can be improved by working on larger data sets and different deep learning architectures in the future.