Diabetic retinopathy (DR) is a severe and progressive complication of diabetes, often leading to vision impairment if not diagnosed and treated in its early stages. Early detection of DR is crucial for effective intervention and prevention of visual loss. This project explores the application of deep learning techniques, specifically convolutional neural networks (CNNs), for the early detection of DR. By leveraging a vast dataset of retinal images, the deep learning model learns to discern subtle abnormalities and microaneurysms in the retinal vasculature, enabling timely diagnosis. The project delves into the design and fine-tuning of the CNN architecture, optimization of hyperparameters, and the evaluation of model performance. The outcomes of this project hold the promise of a scalable and efficient solution for early DR detection, potentially revolutionizing the screening and management of this vision-threatening condition. This project contributes to an early screening process by developing a Web-Based Diabetic Retinopathy Detection System for ophthalmologists and other healthcare professionals to provide them with a quick and convenient tool for conducting DR screening tests.