Detection of Diabetic Retinopathy through Segmentation and Detecting the Affected Area through Optic Disc Extraction

Diabetic retinopathy is a severe disease that can cause blindness if not detected and treated early. Although manual detection by ophthalmologists is efficient, it is both time and money consuming. Artificial intelligence-based systems, such as convolutional neural networks, have shown promise in the early detection of diabetic retinopathy. Convolutional neural networks-based segmentation can help in correctly detecting and separating the damaged areas of the retina, allowing for early detection and treatment. The optical disc can be used to locate the damaged area and provide more details about the progression of the disease and potential therapies. It is vital to continue exploring and improving automated strategies for the early detection of diabetic retinopathy in order to improve patient outcomes and lessen the pressure on healthcare systems. This research provides a method for detecting diabetic retinopathy that uses segmentation with convolutional neural networks and optic disc extraction to remove the affected area