Automatic Detection of Diabetic Retinopathy using CNN

Diabetic Retinopathy (DR) remains a significant complication linked to diabetes and is a leading cause of preventable vision loss globally. The early identification of DR is critical for effective clinical intervention; however, the process of manually examining retinal fundus images is both time-intensive and requires specialized knowledge.This study introduces an automated detection system utilizing a convolutional neural network (CNN) to effectively screen for diabetic retinopathy through the use of color fundus retinal images.The proposed system incorporates image preprocessing and denoising techniques to enhance retinal structures and suppress noise, thus enhancing the clarity of pathological characteristics, including microaneurysms, hemorrhages, and exudates.By leveraging the feature-learning capability of deep CNN architectures, the model automatically extracts hierarchical representations from retinal images and performs classification across both binary (DR vs. non-DR) and multiclass severity levels.The experimental findings indicate that the model attains high accuracy in classification and demonstrates strong performance in detecting different stages of diabetic retinopathy.The study's outcomes reveal the potential of deep learning-driven diagnostic tools to aid ophthalmologists in conducting large-scale screening efforts. These tools enable faster and more reliable detection of diabetic retinopathy (DR). They also provide a cost-effective approach for DR diagnosis. This supports early detection and improves overall patient outcomes