Ultraviolet-induced fluorescence mapping of facial porphyrin and sebum using deep-learning segmentation

This study demonstrates the feasibility of using deep learning to detect porphyrins and sebum in facial fluorescence images, with UNet showing more robust performance than pix2pix. While the single-class model's overall IoU score was slightly higher, the two-class approach offered enhanced interpretability. Future work should expand dataset diversity and validate the models with clinical-grade imaging systems.