Rethinking BCC diagnosis: Automating concept‐specific detection of BCC in dermatoscopic images
The RFSD-BCC system improves BCC diagnosis by integrating feature combination models, which enhances both sensitivity and specificity. It offers interpretable diagnoses that bridge AI analysis with clinical practice, significantly improving clinicians' diagnostic accuracy and fostering better patient understanding.
Authors
- Jianglin Zhang (ORCID: https://orcid.org/0000-0003-0434-1729)
- Yang Ying (ORCID: https://orcid.org/0000-0002-6907-0707)
- Kaibin Lin (ORCID: https://orcid.org/0000-0003-1973-9537)
- Zirou Liu
- Chen Liu (ORCID: https://orcid.org/0000-0003-1558-6836)
- Xiao Dong Chen (ORCID: https://orcid.org/0000-0002-0150-0491)
- Hui Hu (ORCID: https://orcid.org/0000-0001-8198-6374)
- Zheng Wang (ORCID: https://orcid.org/0000-0002-1343-5199)
Institutions
- Jinan University (CN)
- Southern University of Science and Technology (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- Hunan First Normal University (CN)
Publication Details
- Journal
- JDDG Journal der Deutschen Dermatologischen Gesellschaft
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1111/ddg.15608
- Primary Topic
- Cutaneous Melanoma Detection and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00