Child Intussusception Feature Detection Model Based on YOLOv5s and Ultrasound Images
For the purpose of helping doctors to quickly identify the lesions of intussusception in children's abdominal ultrasound and achieving the rapid quality inspection of ultrasound diagnosis data,this paper applied the target detection algorithm to detect the"concentric circle"feature of intussusception in children's abdominal ultrasound images.Firstly,a YOLOv5s based detection model for pediatric intussusception was explored,which had the improved precision,recall,F1 score,mAP@0.5,FPS,and parameter quantity compared to Faster RCNN.Furthermore,a bidirectional feature pyramid network was used to solve the detection problem of the"concentric circle"which was difficult to observed by naked eyes.The attention mechanism was added into the YOLOv5s network to form a detection model based on YOLOv5s_BiFPN_SE framework.The accuracy,recall,F1 score,mAP@0.5 could reach 91.33%,90.73%,91.03%,and 88.77%respectively,which represented better performance than YOLOv5s.
万方数据
维普
