Automated diabetic retinopathy severity grading using novel DR-ResNet + deep learning model.

Diabetic retinopathy, a microvascular condition associated with an increased risk of cardiovascular disease, poses a substantial global healthcare challenge. The demand for timely diagnosis has prompted the development of automated solutions due to the scarcity of specialists. In this paper, we introduce a ground-breaking approach to diabetic retinopathy detection – the Diabetic Retinopathy Residual Network (DR-ResNet +). The proposed model leverages the power of deep learning to automatically extract features, achieving optimal results in just seven training epochs. The DR-ResNet + architecture is meticulously designed by incorporating a series of convolutional, pooling, and fully connected layers. Hyperparameter optimization is done using both grid and random search techniques to ensure peak performance. To validate the proposed model's robustness, simulated results are compared with well-established deep learning models, such as GoogleNet, VGG16, and AlexNet, using a comprehensive Kaggle dataset comprising over 35,000 retinal images. Moreover, the proposed model is also tested on external datasets like MESSIDOR and IDRiD for its validation. Simulation results reveal that the proposed DR-ResNet + model not only reduces training time by an impressive 95% but also exhibits outstanding performance metrics, including an accuracy of 0.9898, specificity of 0.9916, precision of 0.9670, sensitivity of 0.9829, and an F1-score of 0.9748. These findings position the proposed model as exceptionally well-suited for real-time clinical applications, offering a potential game-changer in diabetic retinopathy diagnosis. This paper presents DR-ResNet + as a pioneering advancement in diabetic retinopathy diagnosis. With its rapid training, superior accuracy, and significant real-world implications, the model holds promise for transforming the landscape of healthcare by providing timely and precise diagnoses for this critical condition. [ABSTRACT FROM AUTHOR]