Dual-stage segmentation and classification framework for skin lesion analysis using deep neural network
The proposed dual-stage deep learning framework demonstrates high accuracy and robustness across segmentation and classification tasks on diverse datasets. Its adaptability to large-scale, non-dermoscopic data such as SLICE-3D confirms its potential for deployment in real-world skin cancer triage and teledermatology applications.
Authors
- Marco Agus (ORCID: https://orcid.org/0000-0003-2752-3525)
- Jens Schneider (ORCID: https://orcid.org/0000-0002-0546-2816)
- Nauman Ullah Gilal (ORCID: https://orcid.org/0000-0002-8197-4696)
- Khadija Manzoor (ORCID: https://orcid.org/0009-0009-5430-9795)
Institutions
- Hamad bin Khalifa University (QA)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251351858
- Citations
- 6
- Primary Topic
- Cutaneous Melanoma Detection and Management
- Type
- article
- Field-Weighted Citation Impact
- 6.27
Funders
- Qatar National Library