Enhancing placental pathology detection with GAMatrix-YOLOv8 model
GAMatrix-YOLOv8 achieves unprecedented prediction accuracy in detecting delayed villous maturation in placental pathological tissues through algorithmic innovation. The graphical user interface allows users to upload images, view model prediction results in real time, and analyze and verify test outcomes. This provides a theoretical and technical foundation for further research into artificial intelligence-based pathological auxiliary diagnostic systems.
Authors
- Xiaoyan Wei (ORCID: https://orcid.org/0000-0001-5535-4197)
- Jiahui Ji (ORCID: https://orcid.org/0000-0002-0348-2370)
- Jingwen Liu (ORCID: https://orcid.org/0000-0002-5513-8803)
- Zhifa Jiang (ORCID: https://orcid.org/0000-0002-1817-6413)
- Xiangyun Ye
- Zhen Zhang (ORCID: https://orcid.org/0009-0005-4814-8834)
- Weirui Wu
Institutions
- Guangzhou Women and Children Medical Center (CN)
- Huizhou Central People's Hospital (CN)
- Guangzhou Medical University (CN)
- Huizhou University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1016/j.heliyon.2025.e42441
- Citations
- 3
- Primary Topic
- Smart Systems and Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 4.46
Funders
- Basic and Applied Basic Research Foundation of Guangdong Province