中文

Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays

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  • Release time:2020-09-23

  • Affiliation of Author(s):中南大学

  • Journal:Pattern Recognition,2020

  • Abstract:The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia (CAP) on chest X-rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID-19. A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs: the Discrimination-DL and the Localization-DL. The first DL was developed to extract lung features from chest X-ray radiographs for COVID-19 discrimination and trained using 3548 chest X-ray radiographs. The second DL was trained with 406-pixel patches and applied to the recognized X-ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X-ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists’ discrimination and localization results, the accuracy of COVID-19 discrimination using the Discrimination-DL yielded 98.71%, while the accuracy of localization using the Localization-DL was 93.03%. This work represents the feasibility of using a novel deep learning-based CAD scheme to efficiently and accurately distinguish COVID-19 from CAP and detect localization with high accuracy and agreement with radiologists.

  • Co-author:Xiaowei Liu, Jie Zhang, Yong Li, Ying Xiao

  • First Author:Zheng Wang

  • Indexed by:Journal paper

  • Correspondence Author:Muzhou Hou

  • Document Type:J

  • Page Number:1-9

  • Translation or Not:no

  • Date of Publication:2020-08-30

  • Included Journals:SCI


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