强化特征提取能力的下颌骨骨折检测3M-YOLOv5网络

周涛; 杜玉虎; 石道宗; 彭彩月; 陆惠玲

doi:10.37188/OPE.20233121.3178

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强化特征提取能力的下颌骨骨折检测3M-YOLOv5网络

信息科学 | 更新时间：2023-11-16

- 强化特征提取能力的下颌骨骨折检测3M-YOLOv5网络
- Mandibular fracture detection with 3M-YOLOv5 network based on enhanced feature extraction capability
- 光学精密工程 2023年31卷第21期页码：3178-3191
- 作者机构：
  
  1.北方民族大学计算机科学与工程学院，宁夏银川 750021
  2.北方民族大学图像图形智能处理国家民委重点实验室，宁夏银川 750021
  3.宁夏医科大学医学信息与工程学院，宁夏银川 750004
- 作者简介：
  
  [ "周涛（1977—），男，博士，教授，博士生导师，CSIG理事，CSS理事，主要从事计算机辅助诊断，医学图像分析与处理，模式识别等方面的研究。E-mail：zhoutaonxmu@126.com" ]
  [ "杜玉虎（1999—），男，硕士研究生，主要从事图像图形智能处理方面的研究。E-mail：cy_dyh@163.com" ]
- 基金信息：
  
  国家自然科学基金(62062003);宁夏自然科学基金资助项目(2022AAC03149)
- DOI：10.37188/OPE.20233121.3178
  中图分类号：
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周涛,杜玉虎,石道宗等.强化特征提取能力的下颌骨骨折检测3M-YOLOv5网络[J].光学精密工程,2023,31(21):3178-3191.

ZHOU Tao,DU Yuhu,SHI Daozong,et al.Mandibular fracture detection with 3M-YOLOv5 network based on enhanced feature extraction capability[J].Optics and Precision Engineering,2023,31(21):3178-3191.
周涛,杜玉虎,石道宗等.强化特征提取能力的下颌骨骨折检测3M-YOLOv5网络[J].光学精密工程,2023,31(21):3178-3191. DOI： 10.37188/OPE.20233121.3178.

ZHOU Tao,DU Yuhu,SHI Daozong,et al.Mandibular fracture detection with 3M-YOLOv5 network based on enhanced feature extraction capability[J].Optics and Precision Engineering,2023,31(21):3178-3191. DOI： 10.37188/OPE.20233121.3178.

摘要

针对人工智能辅助骨折部位治疗时由于骨折部位通常伴随着出血等症状，不同体位所拍摄的CT影像存在较大差异，骨折部位大小不一，以及受到出血部位以及周围组织的干扰，骨折部位的特征提取不充分、骨折部位检测精度不高的问题，设计了一种3M-YOLOv5网络来检测下颌骨骨折部位。在特征提取网络中采用密集模块，利用密集连接特性提高网络的特征提取能力；采用局部全局注意力模块来提取CT影像的全局信息；构造一个轻量化的多尺度密集块，以较少的参数量提取骨折部位的多尺度特征；在特征增强网络中设计跨维度双向特征融合模块，使得特征图的高度、宽度以及通道之间有所交互，同时引入可训练的权重来平衡不同尺度特征图的融合重要性。为了验证3M-YOLOv5网络的有效性，在自建数据集上进行消融实验和对比实验。实验结果表明，在置信度阈值取0.5时，3M-YOLOv5网络的mAP值、F1值、召回率、精确率分别为99.17%，99.06%，98.81%和99.32%。所提出的下颌骨骨折CT影像检测网络能够较好地检测出影像中的骨折部位，辅助医生制定治疗方案。

Abstract

For artificial intelligence assistance in the detection of fracture sites， the fracture sites are usually accompanied by bleeding and other symptoms. Further， CT images taken in different positions have large differences， the size of fracture sites varies， and the bleeding sites and surrounding tissues may interfere with the detection of fracture sites， leading to insufficient feature extraction and the problem of low detection accuracy. Therefore， the 3M-YOLOv5 network is designed to detect mandibular fracture sites. First， the dense module is used in the feature extraction network to improve the feature extraction capability of the network by using the dense connection property. The local and global attention module （lgaM） is used to extract the global information of CT images. Second， a lightweight multiscale dense block （lmdM） is designed to extract the multiscale features of the fracture sites with fewer parameters. Third， a cross-dimension bidirectional feature fusion module （cdbfM） is designed in the feature enhancement network to make the height， width， and channel of the feature maps interact with each other， and trainable weights are introduced to balance the fusion importance of the feature maps with different scales. Finally， to verify the effectiveness of the proposed network， ablation and comparison experiments are conducted on a self-built dataset. The results show that when the confidence threshold is 0.5， the mAP value， F1 value， recall rate， and precision rate of the proposed network are 99.17%， 99.06%， 98.81%， and 99.32%， respectively. The proposed CT image detection network for mandibular fracture can better detect the fracture sites in the image than existing methods， which is a good reference for doctors to make a corresponding treatment plan based on the detection results.

关键词

目标检测下颌骨骨折YOLOv5跨维度注意力密集连接神经网络

Keywords

target detectionmandibular fractureYOLOv5cross dimension attentiondensely connected neural network

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