AI-driven tools for non-invasive skin analysis: A study in detecting lentigines and nevi in human skin

dc.contributor.authorSilva, Pedro
dc.contributor.authorSilva, Liliana
dc.contributor.authorVieira, Pedro
dc.contributor.authorPinto, Pedro
dc.date.accessioned2026-02-20T11:57:46Z
dc.date.available2026-02-20T11:57:46Z
dc.date.issued2025-04
dc.description.abstractThis study demonstrates that deep learning models, particularly YOLOv4 and Faster R-CNN, can effectively detect and segment facial hyperpigmentation with high accuracy. The integration of classical image processing and a user-friendly GUI makes the system accessible to clinicians and researchers. These results highlight the potential of AI to enhance dermatological diagnostics and support longitudinal skin health monitoring.por
dc.identifier.authoremailpedro.silva@uevora.pt
dc.identifier.authoremailnd
dc.identifier.authoremailnd
dc.identifier.authoremailnd
dc.identifier.citationProceedings Proceedings book of the IFSCC Cannes Congress 2025por
dc.identifier.scientificarea233por
dc.identifier.sharewithCHRCpor
dc.identifier.urihttp://hdl.handle.net/10174/41340
dc.language.isoporpor
dc.peerreviewednopor
dc.rightsembargoedAccesspor
dc.subjecthiperpigmentationpor
dc.subjectdetectionpor
dc.subjectsegmentationpor
dc.subjectdeep learningpor
dc.titleAI-driven tools for non-invasive skin analysis: A study in detecting lentigines and nevi in human skinpor
dc.typearticle

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