Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/41340
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| Title: | AI-driven tools for non-invasive skin analysis: A study in detecting lentigines and nevi in human skin |
| Authors: | Silva, Pedro
Silva, Liliana
Vieira, Pedro
Pinto, Pedro |
| Keywords: | hiperpigmentation
detection
segmentation
deep learning |
| Issue Date: | Apr-2025 |
| Citation: | Proceedings Proceedings book of the IFSCC Cannes Congress 2025 |
| 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. |
| URI: | http://hdl.handle.net/10174/41340 |
| Type: | article |
| Appears in Collections: | DCMS - Artigos em Livros de Actas/Proceedings
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