Effects of Image Degradation on Deep Neural Network Classification of Scaphoid Fracture Radiographs: Comparison Study of Different Noise Types
The findings indicate that image quality, especially resolution and blurring, substantially influences the robustness of deep learning-based fracture detection models. Ensuring adequate image resolution and quality control can enhance diagnostic reliability. These results provide valuable insights for designing more accurate and resilient medical imaging models under real-world variability.
Authors
- Alfred P. Yoon (ORCID: https://orcid.org/0000-0003-3908-4865)
- Ta‐Chung Chao (ORCID: https://orcid.org/0000-0003-1451-1939)
- Chi‐Hung Lin (ORCID: https://orcid.org/0000-0003-1079-7327)
- Chang‐Fu Kuo (ORCID: https://orcid.org/0000-0002-9770-5730)
- Kevin C. Chung (ORCID: https://orcid.org/0000-0002-2822-3164)
- Chien-Wei Wang (ORCID: https://orcid.org/0000-0002-5771-0340)
Institutions
- University of Nottingham (GB)
- Chang Gung University (TW)
- Chang Gung Memorial Hospital (TW)
- Taoyuan Chang Gung Memorial Hospital (TW)
- Comprehensive Urology (US)
- University of California Davis Medical Center (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-12-08
- DOI
- https://doi.org/10.2196/65596
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00