CyberSight An AI Powered Diabetic Retinopathy Detection System

Abstract

CyberSight is an AI-powered clinical decision support system developed to assist ophthalmologists and general physicians in the early detection and severity grading of diabetic retinopathy using retinal fundus images. Diabetic retinopathy is one of the leading causes of blindness worldwide, especially among diabetic patients who do not receive timely screening and treatment. As the number of diabetes cases continues to rise globally, healthcare systems face increasing pressure to provide faster and more accessible retinal screening services. In many rural and under-resourced areas, the shortage of trained specialists makes regular eye examinations difficult, which often results in delayed diagnosis and permanent vision damage. CyberSight aims to address this challenge by providing an intelligent and accessible platform that supports doctors in identifying diabetic retinopathy at an earlier stage while ensuring that the final medical decision always remains under professional supervision. The system is designed using a modern web-based architecture that combines a React frontend with a FastAPI backend for smooth communication and efficient request handling. The core AI component is based on a Vision Transformer (ViT) deep learning model trained to classify retinal fundus images into five diabetic retinopathy severity categories, ranging from no diabetic retinopathy to proliferative diabetic retinopathy. Before prediction, CyberSight performs several validation steps to improve reliability and reduce errors during inference. The uploaded image is checked for supported formats such as JPEG and PNG, image quality and resolution are verified, and a CLIP-based zero-shot validation mechanism confirms that the uploaded image is actually a retinal fundus image. These validation measures help prevent unrelated or poor-quality images from affecting prediction accuracy and improve the overall robustness of the system. In addition to accurate prediction, CyberSight also focuses on transparency and explainability, which are important challenges in medical AI systems. Many deep learning models are often criticized for behaving like “black boxes” because doctors cannot easily understand how predictions are generated. To solve this issue, CyberSight integrates an occlusion-based explainable AI approach that generates visual heatmaps highlighting the image regions that most influenced the model’s decision. This allows doctors to better interpret and verify AI predictions, improving trust and usability in real clinical environments. The platform also supports automatic diagnostic report generation that includes confidence scores, severity explanations, and medical disclaimers. These reports simplify communication between doctors and patients and make clinical documentation easier. Overall, CyberSight demonstrates how artificial intelligence can be integrated into healthcare systems as a supportive tool to improve early detection, enhance screening efficiency, and assist medical professionals in providing better patient care.