Diabetic Retinopathy Recognition and Classification Using Transfer Learning Deep Neural Networks

Diabetic retinopathy is one of the common causes of blindness with diabetes. Early diagnosis is important to prevent irreversible vision loss. Conventional methods for diagnosing diabetic retinopathy are often based on manual examination of retinal images, which can be time-consuming and subject to human error. The integration of machine-based automated diagnostic systems offers a promising solution to this challenge. Machine-based automated diagnosis of diabetic retinopathy can prevent vision loss with early detection and treatment. In this study, we investigated the performance of different transfer learning models-DenseNet, EfficientNet, VggNet, and ResNet-on a large dataset called Diabetic retinopathy from Kaggle, consisting of 35,108 retinal images in 5 classes. Out of which 28086 samples were used for training purpose and 7,022 samples for validation testing. While previous research has explored machine learning for retinopathy diagnosis, our research uniquely combines modern transfer learning models and evaluates the effectiveness of specific processing methods with Ben Graham's processing methods. This combination distinguishes us from existing methods by contributing to a significant increase inAaccuracy. In particular the accuracy of the proposed approach is 97.7%, Aour tests show that the diagnostic accuracy increases by about 4-5% when using Ben Graham preprocessing. The results of our research may help develop more accurate and efficient automated systems for diagnosing diabetic retinopathy, thereby improving patient outcomes.