Lung nodule detection based on 3D convolutional neural networks

Lei Fan; Zhaoqiang Xia; Xiaobiao Zhang; Xiaoyi Feng

doi:10.1109/FADS.2017.8253184

Lung nodule detection based on 3D convolutional neural networks

Lei Fan
, Zhaoqiang Xia
, Xiaobiao Zhang
, Xiaoyi Feng

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Scopus citations

Abstract

With the increasing number of lung cancer patients, the CAD system is playing an increasingly important rule in the field of automatic identification for medical images. Since the 3D characteristics of low-dose CT images make the 3D convolution more suitable than 2D convolution, in this paper, we propose a method to detect lung nodule of lung CT images using 3D convolutional neural networks. Combined with the traditional morphological preprocessing methods, 3D convolutional neural networks are applied to lung CT images. The experimental accuracy indicates that this method is suitable for the problem of lung nodule detection and has great room to improve. The experimental results also demonstrate that the application of deep learning in the medical field will bring great progress for medical development.

Original language	English
Title of host publication	Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	7-10
Number of pages	4
ISBN (Electronic)	9781538631485
DOIs	https://doi.org/10.1109/FADS.2017.8253184
State	Published - 1 Jul 2017
Event	2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017 - Xian, China Duration: 23 Oct 2017 → 25 Oct 2017

Publication series

Name	Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017
Volume	2018-January

Conference

Conference	2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017
Country/Territory	China
City	Xian
Period	23/10/17 → 25/10/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

3D convolutional network
CT lung image
deep learning
nodule detection

Access to Document

10.1109/FADS.2017.8253184

Cite this

Fan, L., Xia, Z., Zhang, X., & Feng, X. (2017). Lung nodule detection based on 3D convolutional neural networks. In Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017 (pp. 7-10). (Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FADS.2017.8253184

Fan, Lei ; Xia, Zhaoqiang ; Zhang, Xiaobiao et al. / Lung nodule detection based on 3D convolutional neural networks. Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 7-10 (Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017).

@inproceedings{2ccf284d0c734edc880aadfed635a0ea,

title = "Lung nodule detection based on 3D convolutional neural networks",

abstract = "With the increasing number of lung cancer patients, the CAD system is playing an increasingly important rule in the field of automatic identification for medical images. Since the 3D characteristics of low-dose CT images make the 3D convolution more suitable than 2D convolution, in this paper, we propose a method to detect lung nodule of lung CT images using 3D convolutional neural networks. Combined with the traditional morphological preprocessing methods, 3D convolutional neural networks are applied to lung CT images. The experimental accuracy indicates that this method is suitable for the problem of lung nodule detection and has great room to improve. The experimental results also demonstrate that the application of deep learning in the medical field will bring great progress for medical development.",

keywords = "3D convolutional network, CT lung image, deep learning, nodule detection",

author = "Lei Fan and Zhaoqiang Xia and Xiaobiao Zhang and Xiaoyi Feng",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017 ; Conference date: 23-10-2017 Through 25-10-2017",

year = "2017",

month = jul,

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doi = "10.1109/FADS.2017.8253184",

language = "英语",

series = "Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017",

}

Fan, L, Xia, Z, Zhang, X & Feng, X 2017, Lung nodule detection based on 3D convolutional neural networks. in Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017. Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 7-10, 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017, Xian, China, 23/10/17. https://doi.org/10.1109/FADS.2017.8253184

Lung nodule detection based on 3D convolutional neural networks. / Fan, Lei; Xia, Zhaoqiang; Zhang, Xiaobiao et al.
Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 7-10 (Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Lung nodule detection based on 3D convolutional neural networks

AU - Fan, Lei

AU - Xia, Zhaoqiang

AU - Zhang, Xiaobiao

AU - Feng, Xiaoyi

PY - 2017/7/1

Y1 - 2017/7/1

N2 - With the increasing number of lung cancer patients, the CAD system is playing an increasingly important rule in the field of automatic identification for medical images. Since the 3D characteristics of low-dose CT images make the 3D convolution more suitable than 2D convolution, in this paper, we propose a method to detect lung nodule of lung CT images using 3D convolutional neural networks. Combined with the traditional morphological preprocessing methods, 3D convolutional neural networks are applied to lung CT images. The experimental accuracy indicates that this method is suitable for the problem of lung nodule detection and has great room to improve. The experimental results also demonstrate that the application of deep learning in the medical field will bring great progress for medical development.

AB - With the increasing number of lung cancer patients, the CAD system is playing an increasingly important rule in the field of automatic identification for medical images. Since the 3D characteristics of low-dose CT images make the 3D convolution more suitable than 2D convolution, in this paper, we propose a method to detect lung nodule of lung CT images using 3D convolutional neural networks. Combined with the traditional morphological preprocessing methods, 3D convolutional neural networks are applied to lung CT images. The experimental accuracy indicates that this method is suitable for the problem of lung nodule detection and has great room to improve. The experimental results also demonstrate that the application of deep learning in the medical field will bring great progress for medical development.

KW - 3D convolutional network

KW - CT lung image

KW - deep learning

KW - nodule detection

UR - https://www.scopus.com/pages/publications/85050249318

U2 - 10.1109/FADS.2017.8253184

DO - 10.1109/FADS.2017.8253184

M3 - 会议稿件

AN - SCOPUS:85050249318

T3 - Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017

SP - 7

EP - 10

BT - Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017

Y2 - 23 October 2017 through 25 October 2017

ER -

Fan L, Xia Z, Zhang X, Feng X. Lung nodule detection based on 3D convolutional neural networks. In Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 7-10. (Conference Proceedings - 2017 International Conference on the Frontiers and Advances in Data Science, FADS 2017). doi: 10.1109/FADS.2017.8253184

Lung nodule detection based on 3D convolutional neural networks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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