Objective:To realize the optimization and upgradation of the detection method of you only look once(YOLO)algorithm model based on the improved YOLO network on the ultrasound image for breast tumor.Methods:A total of 659 images of breast tumor of the Kaggle database were selected as the initially dataset,and the image annotation tool Labelimg was used to conduct pre-labeling for the detection targets in the images.According to a ratio as 7:3,629 images of the 659 images were divided into the train set and validation set,and the other 30 images were used as the test set.The convolutional block attention module(CBAM)and bidirectional feature pyramid network(BiFPN)were introduced into the original YOLO algorithm to underwent structural improvement,which was named as YOLOv5-BiFPN-CBAM.Both the train set and validation set were placed in original YOLO algorithm model and YOLOv5-BiFPN-CBAM model to conduct train,which included 200 rounds of iterative training.The obtained optimal weight files were used in the final test of test set.Results:After 200 rounds of iterative train for two kinds of models,the test results of validation set indicated that the mean values of average precision of two kinds of models were respectively 72.1%and 80.5%for all ultrasound images of breast tumor.The result,that the optimal weight file of improved model was tested by test set,indicated the test ability of improved model was significantly enhanced than that of original model for small target in image.Conclusion:Compared with the original YOLO algorithm model,the improved YOLO algorithm model has higher recognition capability for image,which also enhances precision and sensitivity in identifying small targets of ultrasound images of breast tumor.This model is helpful to improve the diagnostic efficiency in clinical practice for breast tumor.