PNet: An Efficient Network for Pneumonia Detection

Zhongliang Li; Juan Yu; Xuechen Li; Yingqi Li; Weicai Dai; Linlin Shen; Lisha Mou; Zuhui Pu

doi:10.1109/CISP-BMEI48845.2019.8965660

^*Corresponding author for this work

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

13 Citations (Scopus)

Abstract

Pneumonia is a common lung disease and affects millions of people worldwide each year. The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosing lung diseases. Usually, chest X-ray images (CXRs) are taken at physical examination and evaluated by radiologists. The unbalance between tremendous numbers of CXRs and limited number of radiologists need to be solved by Computer Assisted Diagnosis (CAD). As deep neural networks have shown promising results in CAD, we implemented a deep learning-based framework, PNet, for pneumonia detection. 10784 chest X-ray images collected at the Shenzhen No.2 People's Hospital were employed for training and evaluation. The experimental results showed that the proposed PNet (1,695,777 parameters) achieved higher accuracy and F1 score than classical networks like AlexNet (42,725,889 parameters) and VGG16 (27,560,769 parameters).

Original language	English
Title of host publication	Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
Editors	Qingli Li, Lipo Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728148526
DOIs	https://doi.org/10.1109/CISP-BMEI48845.2019.8965660
Publication status	Published - Oct 2019
Externally published	Yes
Event	12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 - Huaqiao, China Duration: 19 Oct 2019 → 21 Oct 2019

Publication series

Name	Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

Conference

Conference	12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
Country/Territory	China
City	Huaqiao
Period	19/10/19 → 21/10/19

UN SDGs

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

SDG 3 Good Health and Well-being

Free Keywords

Chest X-ray image
Convolutional Neural Network
Pneumonia
Pneumonia detection

ASJC Scopus subject areas

Information Systems
Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Information Systems and Management

Access to Document

10.1109/CISP-BMEI48845.2019.8965660

Cite this

Li, Z., Yu, J., Li, X., Li, Y., Dai, W., Shen, L., Mou, L., & Pu, Z. (2019). PNet: An Efficient Network for Pneumonia Detection. In Q. Li, & L. Wang (Eds.), Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 Article 8965660 (Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISP-BMEI48845.2019.8965660

Li, Zhongliang ; Yu, Juan ; Li, Xuechen et al. / PNet : An Efficient Network for Pneumonia Detection. Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019. editor / Qingli Li ; Lipo Wang. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019).

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title = "PNet: An Efficient Network for Pneumonia Detection",

abstract = "Pneumonia is a common lung disease and affects millions of people worldwide each year. The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosing lung diseases. Usually, chest X-ray images (CXRs) are taken at physical examination and evaluated by radiologists. The unbalance between tremendous numbers of CXRs and limited number of radiologists need to be solved by Computer Assisted Diagnosis (CAD). As deep neural networks have shown promising results in CAD, we implemented a deep learning-based framework, PNet, for pneumonia detection. 10784 chest X-ray images collected at the Shenzhen No.2 People's Hospital were employed for training and evaluation. The experimental results showed that the proposed PNet (1,695,777 parameters) achieved higher accuracy and F1 score than classical networks like AlexNet (42,725,889 parameters) and VGG16 (27,560,769 parameters).",

keywords = "Chest X-ray image, Convolutional Neural Network, Pneumonia, Pneumonia detection",

author = "Zhongliang Li and Juan Yu and Xuechen Li and Yingqi Li and Weicai Dai and Linlin Shen and Lisha Mou and Zuhui Pu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 ; Conference date: 19-10-2019 Through 21-10-2019",

year = "2019",

month = oct,

doi = "10.1109/CISP-BMEI48845.2019.8965660",

language = "English",

series = "Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019",

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

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booktitle = "Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019",

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Li, Z, Yu, J, Li, X, Li, Y, Dai, W, Shen, L, Mou, L & Pu, Z 2019, PNet: An Efficient Network for Pneumonia Detection. in Q Li & L Wang (eds), Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019., 8965660, Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019, Institute of Electrical and Electronics Engineers Inc., 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019, Huaqiao, China, 19/10/19. https://doi.org/10.1109/CISP-BMEI48845.2019.8965660

PNet: An Efficient Network for Pneumonia Detection. / Li, Zhongliang; Yu, Juan; Li, Xuechen et al.
Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019. ed. / Qingli Li; Lipo Wang. Institute of Electrical and Electronics Engineers Inc., 2019. 8965660 (Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019).

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

TY - GEN

T1 - PNet

T2 - 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

AU - Li, Zhongliang

AU - Yu, Juan

AU - Li, Xuechen

AU - Li, Yingqi

AU - Dai, Weicai

AU - Shen, Linlin

AU - Mou, Lisha

AU - Pu, Zuhui

PY - 2019/10

Y1 - 2019/10

N2 - Pneumonia is a common lung disease and affects millions of people worldwide each year. The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosing lung diseases. Usually, chest X-ray images (CXRs) are taken at physical examination and evaluated by radiologists. The unbalance between tremendous numbers of CXRs and limited number of radiologists need to be solved by Computer Assisted Diagnosis (CAD). As deep neural networks have shown promising results in CAD, we implemented a deep learning-based framework, PNet, for pneumonia detection. 10784 chest X-ray images collected at the Shenzhen No.2 People's Hospital were employed for training and evaluation. The experimental results showed that the proposed PNet (1,695,777 parameters) achieved higher accuracy and F1 score than classical networks like AlexNet (42,725,889 parameters) and VGG16 (27,560,769 parameters).

AB - Pneumonia is a common lung disease and affects millions of people worldwide each year. The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosing lung diseases. Usually, chest X-ray images (CXRs) are taken at physical examination and evaluated by radiologists. The unbalance between tremendous numbers of CXRs and limited number of radiologists need to be solved by Computer Assisted Diagnosis (CAD). As deep neural networks have shown promising results in CAD, we implemented a deep learning-based framework, PNet, for pneumonia detection. 10784 chest X-ray images collected at the Shenzhen No.2 People's Hospital were employed for training and evaluation. The experimental results showed that the proposed PNet (1,695,777 parameters) achieved higher accuracy and F1 score than classical networks like AlexNet (42,725,889 parameters) and VGG16 (27,560,769 parameters).

KW - Chest X-ray image

KW - Convolutional Neural Network

KW - Pneumonia

KW - Pneumonia detection

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U2 - 10.1109/CISP-BMEI48845.2019.8965660

DO - 10.1109/CISP-BMEI48845.2019.8965660

M3 - Conference contribution

AN - SCOPUS:85079195923

T3 - Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

BT - Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

A2 - Li, Qingli

A2 - Wang, Lipo

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 19 October 2019 through 21 October 2019

ER -

Li Z, Yu J, Li X, Li Y, Dai W, Shen L et al. PNet: An Efficient Network for Pneumonia Detection. In Li Q, Wang L, editors, Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8965660. (Proceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019). doi: 10.1109/CISP-BMEI48845.2019.8965660