Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot
Among the three deep learning models, the Mask2former model demonstrated the best overall performance. This method can effectively assist clinicians in recognizing DFU and segmenting the tissues within the wounds.
Authors
- Xiao Li (ORCID: https://orcid.org/0000-0002-3829-2841)
- Huijuan Zhu (ORCID: https://orcid.org/0000-0001-5172-6870)
- Guangxin Zhou (ORCID: https://orcid.org/0000-0002-0252-2473)
- Yongsheng Tao (ORCID: https://orcid.org/0000-0001-5840-2254)
- Xiaowen Wang (ORCID: https://orcid.org/0000-0002-3886-8919)
- Baolin Du (ORCID: https://orcid.org/0000-0002-4145-2121)
- Jinzheng Hou
- Da Zhang (ORCID: https://orcid.org/0000-0002-0805-7066)
- Na Zhao (ORCID: https://orcid.org/0000-0003-2329-7014)
Institutions
- Air Force General Hospital PLA (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-04-09
- DOI
- https://doi.org/10.3389/fendo.2025.1543192
- Citations
- 7
- Primary Topic
- Diabetic Foot Ulcer Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 9.06