Diabetic Retinopathy Prediction via a Deep Learning Web Application: A Practical Implementation Leveraging Flask and PyTorch

Diabetic Retinopathy (DR) stands as a primary cause of vision impairment on a global scale. The timely identification of DR through automated screening methodologies holds significant promise for enhancing patient treatment outcomes. This paper details the development of a web-based application employing deep learning for the prediction of Diabetic Retinopathy severity from digital images of the ocular fundus. Constructed using the Flask web framework and the PyTorch deep learning library, the system utilizes a carefully fine-tuned ResNet18 model to categorize retinal images into one of five distinct stages of DR severity. The user interface of the application is designed with a modern, dark-themed aesthetic, facilitating seamless image uploading, real-time diagnostic predictions, and temporary visualization of the uploaded image. This research showcases a streamlined, readily accessible, and scalable solution for DR detection, envisioned for integration within telemedicine platforms or as an initial screening tool in various healthcare settings.