Diabetic Retinopathy Detection

Abstract

In this pivotal study, we delve into the imperative realm of Diabetic Retinopathy (DR), a sight-threatening eye disease, introducing a nuanced and comprehensive approach to its detection through cutting-edge deep learning techniques. Our focus centers on the meticulous comparison of four distinguished convolutional neural network (CNN) architectures: CNN, VGG16, ResNet50, and EfficientNetB5. The dataset comprises five meticulously curated image categories, delineating various DR stages. The discerning outcomes illuminate the consistent superiority of the CNN and VGG16 algorithms, showcasing a commendable edge in accuracy. To amplify DR detection precision, we introduce an innovative hybrid model amalgamating the robust attributes of both CNN and VGG16. This hybrid model triumphs with an extraordinary accuracy rate of 90.5%, surpassing individual performances of the four algorithms. In summation, our groundbreaking study unequivocally establishes the efficacy of a hybrid CNN-VGG16 model for Diabetic Retinopathy detection, underscoring its substantial superiority in accuracy when compared to each of the individual algorithms, advocating for the strategic integration of diverse deep learning models to fortify the accuracy and dependability of medical image analysis. The repercussions of our findings promise to revolutionize the landscape of early detection and holistic management of DR, fulfilling an unmet healthcare need.

Topics & Concepts