[1]曹宇,邢素霞,逄键梁,等.基于改进的VGG-16卷积神经网络的肺结节检测[J].中国医学物理学杂志,2020,37(7):940-944.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.026]
 CAO Yu,XING Suxia,PANG Jianliang,et al.Detection of pulmonary nodules based on improved VGG-16 convolution neural network[J].Chinese Journal of Medical Physics,2020,37(7):940-944.[doi:DOI:10.3969/j.issn.1005-202X.2020.07.026]
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基于改进的VGG-16卷积神经网络的肺结节检测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第7期
页码:
940-944
栏目:
医学人工智能
出版日期:
2020-07-25

文章信息/Info

Title:
Detection of pulmonary nodules based on improved VGG-16 convolution neural network
文章编号:
1005-202X(2020)07-0940-05
作者:
曹宇1邢素霞1逄键梁2王孝义1王瑜1潘子妍1申楠1
1.北京工商大学计算机与信息工程学院, 北京 100048; 2.空军特色医学中心, 北京 100048
Author(s):
CAO Yu1 XING Suxia1 PANG Jianliang2 WANG Xiaoyi1 WANG Yu1 PAN Ziyan1 SHEN Nan1
1. School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China 2. Air Force Medical Center, Beijing 100048, China
关键词:
肺结节VGG-16极限学习机卷积神经网络
Keywords:
Keywords: pulmonary nodule VGG-16 extreme learning machine convolutional neural network
分类号:
R318;R563.9
DOI:
DOI:10.3969/j.issn.1005-202X.2020.07.026
文献标志码:
A
摘要:
【摘要】针对肺结节特征复杂、人工提取特征困难的问题,提出基于改进的VGG-16卷积神经网络的肺结节检测模型。首先采用阈值分割与处理最大连通区域后的图像进行掩模运算,得到肺实质部分。然后通过Regionprops标记每个连通区域序号分割出所有疑似结节;采用核函数极限学习机而不是Softmax函数作为VGG-16结构中的分类器。最后利用改进后的VGG-16模型去除假阳性结节,完成对肺结节检测。在LIDC-IDRI数据集上进行的实验表明改进后的模型能达到92.56%的准确率和94.44%的高敏感度。该模型可用于辅助医生进行肺结节诊断,具有一定的临床应用价值。
Abstract:
Abstract: Aiming at the complex features of pulmonary nodules and the difficulties of extracting features manually, a pulmonary nodule detection model based on improved VGG-16 convolution neural network is proposed. Firstly, the lung parenchyma is obtained by threshold segmentation and mask operation after processing the image of the maximum connected area. Then, the serial number of each connected area is labeled by Regionprops for obtaining all suspected nodules. Kernel extreme learning machine instead of Softmax function is taken as classifier in VGG-16 architecture. Finally, the improved VGG-16 model is used to remove false positive nodules and complete the detection of pulmonary nodules. The proposed method is tested on LIDC-IDRI dataset, and the results showed that the improved model can achieve an accuracy of 92.56% and a sensitivity up to 94.44%. The proposed model can be used to assist doctors in the diagnosis of pulmonary nodules, and has a certain clinical value.

相似文献/References:

[1]唐思源,刘燕茹,杨敏,等.基于CT图像的肺结节检测与识别[J].中国医学物理学杂志,2019,36(7):800.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.011]
 TANG Siyuan,LIU Yanru,YANG Min,et al.Detection and recognition of pulmonary nodules based on CT images[J].Chinese Journal of Medical Physics,2019,36(7):800.[doi:DOI:10.3969/j.issn.1005-202X.2019.07.011]
[2]张倩雯,陈明,秦玉芳,等.基于3D ResUnet网络的肺结节分割[J].中国医学物理学杂志,2019,36(11):1356.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.021]
 ZHANG Qianwen,CHEN Ming,QIN Yufang,et al.Lung nodule segmentation based on 3D ResUnet network[J].Chinese Journal of Medical Physics,2019,36(7):1356.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.021]

备注/Memo

备注/Memo:
【收稿日期】2020-02-20 【基金项目】首都卫生发展科研专项(首发2018-2-5122) 【作者简介】曹宇,硕士,研究方向:图像处理,E-mail:694750411@qq.com 【通信作者】邢素霞,博士,副教授,研究方向:图像处理,E-mail: xingsuxia@163.com
更新日期/Last Update: 2020-07-28