Objective Early diagnosis of breast cancer is of paramount importance. We aim to utilize deep learning models to accurately detect the nipple area in Automated Breast Ultrasound (ABUS) data, ensuring reliable technical diagnostic support for breast tumors at an early stage. Methods Based on the YOLO series models, we locate and detect the nipple in ABUS coronal plane images, providing a positional reference for tumor diagnosis. Results The YOLO series models have all performed well. Particularly, the YOLOv5s model achieved a high precision rate of 0.955, a recall rate of 0.925, and a frame rate of 243, meeting the clinical diagnostic requirements. Conclusion The YOLOv5 model has demonstrated excellent performance in the ABUS nipple localization task. This technology provides crucial technical support for the early detection of breast tumors, with significant clinical implications.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |