Classification of benign and malignant lung nodules based on residuals and 3D VNet network

Ying Zhou; Zhaokai Kong; Mengyi Zhang; Dongmei Sun; Wenjun Zhu; Tian Wang

doi:10.1109/CAC53003.2021.9727810

Classification of benign and malignant lung nodules based on residuals and 3D VNet network

Ying Zhou
, Zhaokai Kong
, Mengyi Zhang^*
, Dongmei Sun^*
, Wenjun Zhu
, Tian Wang

^*Corresponding author for this work

School of Artificial Intelligence(Institute of Artificial Intelligence)

Nanjing Tech University

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

2 Scopus citations

Abstract

This paper proposes a classification method of lung nodules combining 3D VNet network and residual network. This method first preprocesses the original CT image and sends it to the VNet network for pulmonary nodules detection. Then the detection results are used to complete the classification of benign and malignant lung nodules through the residual network. Finally, the classification results are output. A large number of experiments have shown that this method can well extract the position information of lung nodules in CT images. Based on the LUNA16 data set, this method has achieved high accuracy, sensitivity, and specificity. Numerically, it has achieved better classification results than traditional network models.

Original language	English
Title of host publication	Proceeding - 2021 China Automation Congress, CAC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1555-1559
Number of pages	5
ISBN (Electronic)	9781665426473
DOIs	https://doi.org/10.1109/CAC53003.2021.9727810
State	Published - 2021
Event	2021 China Automation Congress, CAC 2021 - Beijing, China Duration: 22 Oct 2021 → 24 Oct 2021

Publication series

Name	Proceeding - 2021 China Automation Congress, CAC 2021

Conference

Conference	2021 China Automation Congress, CAC 2021
Country/Territory	China
City	Beijing
Period	22/10/21 → 24/10/21

Keywords

Benign
Convolutional Neural Network
Malignant Classification
Pulmonary nodules
VNet

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10.1109/CAC53003.2021.9727810

Cite this

Zhou, Y., Kong, Z., Zhang, M., Sun, D., Zhu, W., & Wang, T. (2021). Classification of benign and malignant lung nodules based on residuals and 3D VNet network. In Proceeding - 2021 China Automation Congress, CAC 2021 (pp. 1555-1559). (Proceeding - 2021 China Automation Congress, CAC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC53003.2021.9727810

@inproceedings{54ad4b8f7ffe43aba4700da920b3f9e0,

title = "Classification of benign and malignant lung nodules based on residuals and 3D VNet network",

abstract = "This paper proposes a classification method of lung nodules combining 3D VNet network and residual network. This method first preprocesses the original CT image and sends it to the VNet network for pulmonary nodules detection. Then the detection results are used to complete the classification of benign and malignant lung nodules through the residual network. Finally, the classification results are output. A large number of experiments have shown that this method can well extract the position information of lung nodules in CT images. Based on the LUNA16 data set, this method has achieved high accuracy, sensitivity, and specificity. Numerically, it has achieved better classification results than traditional network models.",

keywords = "Benign, Convolutional Neural Network, Malignant Classification, Pulmonary nodules, VNet",

author = "Ying Zhou and Zhaokai Kong and Mengyi Zhang and Dongmei Sun and Wenjun Zhu and Tian Wang",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 China Automation Congress, CAC 2021 ; Conference date: 22-10-2021 Through 24-10-2021",

year = "2021",

doi = "10.1109/CAC53003.2021.9727810",

language = "英语",

series = "Proceeding - 2021 China Automation Congress, CAC 2021",

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

pages = "1555--1559",

booktitle = "Proceeding - 2021 China Automation Congress, CAC 2021",

address = "美国",

}

Zhou, Y, Kong, Z, Zhang, M, Sun, D, Zhu, W & Wang, T 2021, Classification of benign and malignant lung nodules based on residuals and 3D VNet network. in Proceeding - 2021 China Automation Congress, CAC 2021. Proceeding - 2021 China Automation Congress, CAC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 1555-1559, 2021 China Automation Congress, CAC 2021, Beijing, China, 22/10/21. https://doi.org/10.1109/CAC53003.2021.9727810

Classification of benign and malignant lung nodules based on residuals and 3D VNet network. / Zhou, Ying; Kong, Zhaokai; Zhang, Mengyi et al.
Proceeding - 2021 China Automation Congress, CAC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1555-1559 (Proceeding - 2021 China Automation Congress, CAC 2021).

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

TY - GEN

T1 - Classification of benign and malignant lung nodules based on residuals and 3D VNet network

AU - Zhou, Ying

AU - Kong, Zhaokai

AU - Zhang, Mengyi

AU - Sun, Dongmei

AU - Zhu, Wenjun

AU - Wang, Tian

PY - 2021

Y1 - 2021

N2 - This paper proposes a classification method of lung nodules combining 3D VNet network and residual network. This method first preprocesses the original CT image and sends it to the VNet network for pulmonary nodules detection. Then the detection results are used to complete the classification of benign and malignant lung nodules through the residual network. Finally, the classification results are output. A large number of experiments have shown that this method can well extract the position information of lung nodules in CT images. Based on the LUNA16 data set, this method has achieved high accuracy, sensitivity, and specificity. Numerically, it has achieved better classification results than traditional network models.

AB - This paper proposes a classification method of lung nodules combining 3D VNet network and residual network. This method first preprocesses the original CT image and sends it to the VNet network for pulmonary nodules detection. Then the detection results are used to complete the classification of benign and malignant lung nodules through the residual network. Finally, the classification results are output. A large number of experiments have shown that this method can well extract the position information of lung nodules in CT images. Based on the LUNA16 data set, this method has achieved high accuracy, sensitivity, and specificity. Numerically, it has achieved better classification results than traditional network models.

KW - Benign

KW - Convolutional Neural Network

KW - Malignant Classification

KW - Pulmonary nodules

KW - VNet

UR - https://www.scopus.com/pages/publications/85128110720

U2 - 10.1109/CAC53003.2021.9727810

DO - 10.1109/CAC53003.2021.9727810

M3 - 会议稿件

AN - SCOPUS:85128110720

T3 - Proceeding - 2021 China Automation Congress, CAC 2021

SP - 1555

EP - 1559

BT - Proceeding - 2021 China Automation Congress, CAC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 China Automation Congress, CAC 2021

Y2 - 22 October 2021 through 24 October 2021

ER -

Classification of benign and malignant lung nodules based on residuals and 3D VNet network

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