At present, breast cancer, with the highest annual incidence, has replaced lung cancer, and the target detection technology based on deep learning can automatically detect lesions on non-invasive imaging such as mammography X-ray, breast ultrasound, and breast magnetic resonance imaging (MRI), and it has become the preferred way for adjuvant diagnosis of breast cancer. You only look once (YOLO) series algorithms are object detection algorithms based on deep learning, and classical YOLO algorithms have certain advantages in speed and accuracy and are widely used in computer vision fields. The latest YOLO algorithm is the state of the art (SOTA) model in the field of computer vision, and how to use YOLO series algorithms to improve the speed and accuracy of breast cancer detection has become one of the focus of researchers. On this basis, this study introduces the principle of the classical YOLO series algorithms, sorts out the application status of the classical YOLO series algorithms in breast cancer image detection, summarizes the existing problems, and looks forward to the further application of the YOLO series algorithms in breast cancer detection.