Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays
作者：
Highlights：
• We proposed a novel framework, CHP-Net, to differentiate and localize COVID-19 from community acquired pneumonia.
• We used excessive data augmentation to extend the available dataset and optimize the CHP-Net generalization capability.
• Comparing to other ConvNet, CHP-Net works much more efficiently to extract feature information on chest X-Ray.
• All metrics, including categorical loss, accuracy, precision, recall and F1-score, proved CHP-Net fits good for the task.
• CHP-Net are better than the previous methods tested in detecting COVID-19 and exceeding to radiologist.
摘要
•We proposed a novel framework, CHP-Net, to differentiate and localize COVID-19 from community acquired pneumonia.•We used excessive data augmentation to extend the available dataset and optimize the CHP-Net generalization capability.•Comparing to other ConvNet, CHP-Net works much more efficiently to extract feature information on chest X-Ray.•All metrics, including categorical loss, accuracy, precision, recall and F1-score, proved CHP-Net fits good for the task.•CHP-Net are better than the previous methods tested in detecting COVID-19 and exceeding to radiologist.
论文关键词：COVID-19,Computer-aided detection (CAD),Community-acquired pneumonia (CAP),Deep learning (DL),Chest X-ray (CXR)
论文评审过程：Received 11 June 2020, Revised 7 August 2020, Accepted 24 August 2020, Available online 26 August 2020, Version of Record 1 November 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107613
