Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays
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- 所属单位：中南大学
- 发表刊物：Pattern Recognition，2020
- 摘要：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.
- 合写作者：Xiaowei Liu, Jie Zhang, Yong Li, Ying Xiao
- 第一作者：Zheng Wang
- 论文类型：期刊论文
- 通讯作者：Muzhou Hou
- 文献类型：J
- 页面范围：1-9
- 是否译文：否
- 发表时间：2020-08-30
- 收录刊物：SCI
