Abstract: Breast cancer is the most common cancer among women worldwide. Ultrasound is widely used as a harmless test for early breast cancer screening. The ultrasound network (USNet) model is presented. It is an improved object detection model specifically for breast nodule detection on ultrasound images. USNet improved the backbone network, optimized the generation of feature maps, and adjusted the loss function. Finally, USNet trained with real clinical data. The evaluation results show that the trained model has strong nodule detection ability. The mean average precision (mAP) value can reach 0.734 9. The nodule detection rate is 95.11%, and the in situ cancer detection rate is 79.65%. At the same time, detection speed can reach 27.3 frame per second (FPS), and the
video data can be processed in real time.

Key words: convolutional neural network, object detection, ultrasound, breast nodule