Date of Publication :17th May 2024

Abstract:This groundbreaking study introduces a novel approach to automated screening and diagnosis of Diabetic Retinopathy (DR) using a sophisticated Generative Adversarial Network (GAN)-based methodology. Using the IDRiD dataset, which offers annotated retinal images, our research aims to develop a robust deep learning model capable of accurately detecting DR severity levels. By employing advanced techniques like SRGAN for image quality enhancement and custom deep learning architectures, we seek to establish a new benchmark for DR screening accuracy. Emphasizing technological innovation in healthcare, this project underscores ethical considerations and identifies avenues for future enhancements in AI-driven diagnostics.

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