<b>Fundus Image-Based Automatic Segmentation</b>

In this study, we formulate the task of retinal disease diagnosis as a supervised multiclass classification problem. The goal is to automatically assign each fundus image to one of four predefined diagnostic categories: HR, DR, papilledema, or normal. This classification is based on quantitative features derived from vessel segmentation maps, including both radiomic descriptors and the AVR. By framing the task in this way, the study aims to develop interpretable and generalizable models that support clinical decision-making across a broad spectrum of retinal pathologies.