Our project is centered around the development of a web-based tool dedicated to the precise segmentation of optic discs within a collection of retinal fundus images. This task holds paramount significance in the early diagnosis of critical eye conditions, including Glaucoma and Diabetic Retinopathy. By harnessing cutting-edge technology and artificial intelligence, our solution streamlines and enhances this essential medical image analysis process.
The process begins with the ingestion of RGB images, which undergo a series of carefully orchestrated steps. First, they are resized to a standardized format, ensuring consistency in input data. Then, a meticulous preprocessing pipeline is applied to isolate the Region of Interest (RoI), which isolates the optic disc for further analysis.
The core of our system is a deep neural network model, fine-tuned for the specific task of optic disc segmentation. This model leverages its architectural complexity to deliver accurate and reliable results, providing healthcare professionals with a valuable tool for early disease detection.
The web application interface offers users a seamless and intuitive experience, allowing them to upload images and obtain segmented results effortlessly. Our project stands at the intersection of cutting-edge technology and healthcare, contributing to the early identification and management of sight-threatening conditions, ultimately improving patient outcomes and quality of life.