基于改进YOLOv8n的儿童肠套叠B型超声图像特征检测  

Feature detection of B-ultrasound images of intussusception in children based on improved YOLOv8n

作　　者：刘晨雨 徐健[1] 李轲 王璐 LIU Chenyu;XU Jian;LI Ke;WANG Lu(School of Electronic Information,Xi’an Polytechnic University,Xi’an 710600,P.R.China;Department of Ultrasound Medicine,Xijing Hospital,Air Force Medical University,Xi’an 710000,P.R.China;Department of Ultrasound Medicine,Tangdu Hospital,Air Force Medical University,Xi’an 710038,P.R.China)

机构地区：[1]西安工程大学电子信息学院,西安710600 [2]空军军医大学附属西京医院超声医学科,西安710000 [3]空军军医大学附属唐都医院超声医学科,西安710038

出　　处：《生物医学工程学杂志》2024年第5期903-910,共8页Journal of Biomedical Engineering

基　　金：陕西省科技厅项目(2018GY-173);西安市科技局项目(GXYD7.5)。

摘　　要：为辅助基层超声科医生从儿童腹部超声图像中准确且快速地检测出肠套叠病灶,本文提出了一种基于改进YOLOv8n的儿童肠套叠检测算法EMC-YOLOv8n。首先,采用具有级联分组注意力模块的EfficientViT网络作为主干网络,以提高目标检测速度。其次,利用改进后的C2fMBC模块替换颈部网络中的C2f模块,降低网络复杂度,并在每个C2fMBC模块之后引入坐标注意力机制模块,以增强对位置信息的关注度。最后,在自建的儿童肠套叠数据集上进行实验。结果表明,EMC-YOLOv8n算法的召回率(Recall)、平均检测精度(mAP@0.5)及精确度(Precision)相较基线算法分别提高了3.9%、2.1%及0.9%。尽管网络参数量及计算量略微增加,但检测精度得到显著提升,能够高效完成检测任务,极具经济及社会价值。To assist grassroots sonographers in accurately and rapidly detecting intussusception lesions from children's abdominal ultrasound images,this paper proposes an improved YOLOv8n children's intussusception detection algorithm,called EMC-YOLOv8n.Firstly,the EfficientViT network with a cascaded group attention module was used as the backbone network to enhance the speed of target detection.Secondly,the improved C2fMBC module was used to replace the C2f module in the neck network to reduce network complexity,and the coordinate attention(CA)module was introduced after each C2fMBC module to enhance attention to positional information.Finally,experiments were conducted on the self-built dataset of intussusception in children.The results showed that the recall rate,average detection accuracy(mAP@0.5)and precision of the EMC-YOLOv8n algorithm improved by 3.9%,2.1%and 0.9%,respectively,compared to the baseline algorithm.Despite slightly increased network parameters and computational load,significant improvements in detection accuracy enable efficient completion of detection tasks,demonstrating substantial economic and social value.

关 键 词：肠套叠病灶 EMC-YOLOv8n EfficientViT C2fMBC 坐标注意力机制模块 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] R725.7[医药卫生—儿科]