Diabetic Retinopathy (DR) is a severe eye disease caused by long-term diabetes, leading to vision impairment and potential blindness if not diagnosed early. Automated detection of DR using Deep Learning techniques has gained significant attention for improving screening efficiency and accuracy. This paper presents a study on detecting Diabetic Retinopathy in retinal images using Convolutional Neural Networks (CNNs). CNNs are highly effective in extracting hierarchical visual features from medical images, enabling precise classification of disease severity levels. The proposed approach involves preprocessing retinal fundus images for noise reduction, contrast enhancement, and normalization, followed by training CNN models to detect abnormalities such as microaneurysms, hemorrhages, and exudates. The architecture demonstrates high accuracy and robustness in differentiating between healthy and diseased retina images. Comparative analysis with traditional image processing and machine learning methods shows that CNN-based models significantly outperform earlier approaches in both sensitivity and specificity. This study concludes that deep learning using CNNs provides a reliable, scalable, and automated solution for early DR diagnosis, helping prevent blindness through timely medical intervention.
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