多策略融合的肺结节检测模型与算法

马巧梅; 梁昊然; 王明俊; 程鑫

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多策略融合的肺结节检测模型与算法

更新时间：2026-09-04

- 多策略融合的肺结节检测模型与算法
- Multi-strategy fusion model and algorithm for lung nodule detection
- 微电子学与计算机 2021年38卷第6期页码：38-44
- 作者机构：
  
  1. 中北大学机构软件学院,山西,太原,030051
  2. 山西省军民融合软件技术工程研究中心,山西,太原,030051
- 作者简介：
- 基金信息：
  
  山西省自然科学基金 201801D121026山西省研究生创新项目 2020SY398
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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马巧梅, 梁昊然, 王明俊, 程鑫. 多策略融合的肺结节检测模型与算法[J]. 微电子学与计算机, 2021, 38(6): 38-44.

MA Qiao-mei, LIANG Hao-ran, WANG Ming-jun, et al. Multi-strategy fusion model and algorithm for lung nodule detection[J]. 2021, 38(6): 38-44.
马巧梅, 梁昊然, 王明俊, 程鑫. 多策略融合的肺结节检测模型与算法[J]. 微电子学与计算机, 2021, 38(6): 38-44. DOI：

MA Qiao-mei, LIANG Hao-ran, WANG Ming-jun, et al. Multi-strategy fusion model and algorithm for lung nodule detection[J]. 2021, 38(6): 38-44. DOI：

摘要

肺结节早期检测可以提高病人的生存率，自动检测算法可以有效辅助医生进行诊断.为了提高肺结节检测精确度、降低漏诊率，提出了基于循环残差注意力门机制的U-Net(Recurrent Residual Attention Gate U-Net，R2AGU-Net)肺结节检测模型.首先在原始的U-Net基础上改进，添加循环残差卷积模块并融合注意力门机制，在增强特征提取性能的同时将注意力放在目标结节区域，通过抑制无关的特征响应获得较高的检测精度；其次改进损失函数解决肺结节图像数据不均衡问题，获得较高的检测敏感度；最后通过三维卷积神经网络(3D CNN)分类候选结节，降低检测的假阳性.在两个数据集上进行实验验证，结果表明本文提出的算法提升了检测速度和敏感度，取得了比现有算法更好的性能，具有较好的泛化能力。

Abstract

Early detection of lung nodules can effectively improve the survival rate of patients. Automatic detection algorithm can effectively assist doctors in diagnosis. In the lung nodule detection task

in order to improve the detection accuracy and reduce the missed diagnosis rate

the lung nodule detection algorithm based on the recurrent residual attention gate U-Net (R2AGU-Net) was proposed. Firstly

recurrent residual convolution module and fusion attention gate mechanism were added in U-Net. The improved method can enhance the feature extraction performance

focus on the target nodule area and suppress irrelevant feature responses to obtain higher detection accuracy. Secondly

improved loss function can solve the problem of unbalanced image data of lung nodules to obtain higher detection sensitivity. Finally

the candidate nodules are classified by a three-dimensional convolutional neural network (3D CNN) to reduce false positive. Experimental verification on two datasets shows that the algorithm proposed in this paper improves the detection speed and sensitivity

achieves better performance than existing algorithms

and has better generalization capabilities.

关键词

Keywords

references

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