AI-driven tools for non-invasive skin analysis: A study in detecting lentigines and nevi in human skin
| dc.contributor.author | Silva, Pedro | |
| dc.contributor.author | Silva, Liliana | |
| dc.contributor.author | Vieira, Pedro | |
| dc.contributor.author | Pinto, Pedro | |
| dc.date.accessioned | 2026-02-20T11:57:46Z | |
| dc.date.available | 2026-02-20T11:57:46Z | |
| dc.date.issued | 2025-04 | |
| dc.description.abstract | This 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.authoremail | pedro.silva@uevora.pt | |
| dc.identifier.authoremail | nd | |
| dc.identifier.authoremail | nd | |
| dc.identifier.authoremail | nd | |
| dc.identifier.citation | Proceedings Proceedings book of the IFSCC Cannes Congress 2025 | por |
| dc.identifier.scientificarea | 233 | por |
| dc.identifier.sharewith | CHRC | por |
| dc.identifier.uri | http://hdl.handle.net/10174/41340 | |
| dc.language.iso | por | por |
| dc.peerreviewed | no | por |
| dc.rights | embargoedAccess | por |
| dc.subject | hiperpigmentation | por |
| dc.subject | detection | por |
| dc.subject | segmentation | por |
| dc.subject | deep learning | por |
| dc.title | AI-driven tools for non-invasive skin analysis: A study in detecting lentigines and nevi in human skin | por |
| dc.type | article |