This paper introduces POP-CORN, a novel neural network-based tool for automatically detecting coronal hole boundaries in solar extreme ultraviolet images. By incorporating categorical features of large-scale solar structures, the model accurately determines optimal intensity thresholds for consistent coronal hole identification across different solar cycles, offering a significant advancement in space weather forecasting.