A Deep Learning-Based Automatic Recognition Model for Polycystic Ovary Ultrasound Images
The YOLOv11-based automatic recognition model for PCOS ovarian ultrasound images exhibits strong target detection and diagnostic performance. This approach can streamline the follicle counting process in conventional ultrasound and enhance the efficiency and generalizability of ultrasound-based PCOS assessment.
Authors
- Baihua Zhao (ORCID: https://orcid.org/0000-0001-7185-292X)
- Yingjia Li
- Lieming Wen (ORCID: https://orcid.org/0000-0003-3283-888X)
- Yaqian Fu (ORCID: https://orcid.org/0000-0003-3683-304X)
- Minghui Liu (ORCID: https://orcid.org/0000-0002-6548-3264)
- Jieyu Liu (ORCID: https://orcid.org/0000-0003-4203-9985)
- Shan Zhou (ORCID: https://orcid.org/0000-0001-5795-8277)
- Yunxia Huang
Institutions
- Central South University (CN)
- Second Xiangya Hospital of Central South University (CN)
- Third Xiangya Hospital (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Balkan Medical Journal
- Published
- 2025-08-08
- DOI
- https://doi.org/10.4274/balkanmedj.galenos.2025.2025-5-114
- Citations
- 1
- Primary Topic
- Ovarian function and disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.70