"ResNet-Powered Retinal Image Analysis"

Abstract— Diabetic Retinopathy (DR) is a serious eye condition caused by diabetes that can lead to vision loss if not detected early. Many people with diabetes do not show symptoms in the early stages of DR, which makes timely screening very important. However, traditional screening methods require eye specialists and can take a lot of time and resources. In many areas, there aren’t enough specialists available, which causes delays in diagnosis and treatment. This project introduces RetinoNet, a deep learning-based system that can automatically detect signs of DR from retinal images. Using artificial intelligence, the system analyzes eye images quickly and accurately, helping doctors identify the disease early. This makes screening faster, more accessible, and more reliable. The project also looks into the benefits and challenges of using such AI systems in real- world healthcare, including the need for high- quality data, understanding how the model makes decisions, and how to use it effectively in clinics. Keywords— Diabetic Retinopathy, Deep Learning, Retinal Image Analysis, Automated Diagnosis, Medical Imaging, Health Monitoring, Ophthalmology.