Abstract：Objective To solve the problem of time-consuming and labor in identification of stones and polyps by the use of ultrasound
images of the gallbladder for doctors in the clinical process. Methods Using 200 images collected by of Department of Ultrasound
as the initial data set, the initial data set was first enhanced, the YOLOv5s model was improved by BiFPN structure and EIOU
loss function. Firstly, the initial data set was enhanced, and then the enhanced data set was sent to the improved YOLOv5s model
for training. Results After 300 iterations, the average accuracy of the improved YOLOv5s model in the test set reached 89.79%,
which was significantly improved compared with the same type of model. Conclusion The improved YOLOv5s model proposed
in this paper effectively overcomes the problem of poor detection accuracy of the original model for small and medium targets, and
significantly improves its sensitivity. It can help doctors identify and locate stones and polyps in gallbladder ultrasound images.
