Transfer Learning for Diabetic Retinopathy Detection and Stage Classification

This analysis examines the capabilities of distinct deep learning and transfer learning models in recognizing diabetic retinopathy (DR) using datasets from APTO, Messidor, and RFMiD. A thorough analysis uses a variety of the most recent CNN models, including AlexNet, InceptionV3, InceptionResNetV2, DenseNet201, DenseNet121, Xception, ResNet50, ResNetl52, and ResNet101. Although the initial assessment focused mostly on traditional CNN structures, we expanded this analysis by including the Xception model. In DR grouping errands, we find that Xception works rather well, with precision levels exceeding 100%. This success is attributed to its excellent component learning capabilities and innovative depthwise divisible convolutions. Xception outperforms elective plans, according to a related report, with further developing DR discovery accuracy potential. This analysis uses current deep learning techniques and investigates various model structures to improve clinical image analysis of diabetic retinopathy. The results show that using cutting -edge methods like Xception to improve the effectiveness and sufficiency of DR order frameworks is crucial.