Diabetic Retinopathy (DR) is a major cause of vision impairment worldwide. Early detection and classification of DR cansignificantly reduce the risk of vision loss. This paper presents an implementation of a deep learning-based system for detecting DR usingconvolutional neural networks (CNNs). The proposed method utilizes retinal fundus images for automated classification of different DRstages. The system incorporates preprocessing techniques, data augmentation, and transfer learning with a pre-trained CNN model toenhance accuracy. Experimental results demonstrate the model’s effectiveness in identifying diabetic retinopathy with high sensitivity andspecificity.Keywords: Diabetic Retinopathy, Convolutional Neural Networks, Deep Learning, Image Processing, Retinal Imaging, Medical AI