PNet: An Efficient Network for Pneumonia Detection

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

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 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 languageEnglish
Title of host publicationProceedings - 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
EditorsQingli Li, Lipo Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728148526
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019 - Huaqiao, China
Duration: 19 Oct 201921 Oct 2019

Publication series

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

Conference

Conference12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019
Country/TerritoryChina
CityHuaqiao
Period19/10/1921/10/19

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

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