S-ResNet-34: small sample-ResNet-34 for predicting cervical degeneration in x-ray image data
This study introduced a deep learned-based model, S-ResNet-34, which is an innovative approach to auxiliary diagnosis for small-sample X-ray image analysis. The findings offer a new choice for patients by simplifying the diagnostic process while maintaining diagnostic accuracy.
Authors
- Sizhen Yang
- Tongwei Chu (ORCID: https://orcid.org/0000-0001-7421-1052)
- Han Wu (ORCID: https://orcid.org/0000-0001-5269-9720)
- Liang Chen (ORCID: https://orcid.org/0000-0002-3767-3546)
- Tao Zhang (ORCID: https://orcid.org/0000-0001-5809-0801)
- Yifei Xu (ORCID: https://orcid.org/0009-0009-2405-1113)
- Zhigang Wei (ORCID: https://orcid.org/0000-0002-5046-890X)
Institutions
- Army Medical University (CN)
- Chongqing University (CN)
- Chongqing Vocational Institute of Engineering (CN)
- Xinqiao Hospital (CN)
- Chongqing Cancer Hospital (CN)
Publication Details
- Journal
- BMC Musculoskeletal Disorders
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1186/s12891-025-09273-9
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00