<b>Fundus Image-Based Automatic Segmentation</b>
Version 1In 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 sup
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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 sup