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 3,548 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.

中文翻译：

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通过胸部 X 光检查自动区分和定位 COVID-19 与社区获得性肺炎


COVID-19 疫情继续威胁着全世界人民的健康和生命。当务之急是开发和测试基于深度学习 (DL) 的计算机辅助检测 (CAD) 方案，以通过胸部 X 光检查自动定位和区分 COVID-19 和社区获得性肺炎 (CAP)。因此，本研究旨在开发和测试一种高效、准确的深度学习方案，帮助放射科医生自动识别和定位 COVID-19。从开放图像数据和湘雅医院收集回顾性胸部X射线图像数据集，分为训练组和测试组。所提出的 CAD 框架由 DL 的两个步骤组成：Discrimination-DL 和 Localization-DL。第一个 DL 的开发目的是从胸部 X 射线照片中提取肺部特征以用于区分 COVID-19，并使用 3,548 张胸部 X 射线照片进行训练。第二个深度学习使用 406 像素块进行训练，并将其应用于识别的 X 射线照片，以定位并将它们分配到左肺、右肺或双肺。纳入 CAP 和健康对照的 X 射线照片来评估模型的稳健性。与放射科医生的判别和定位结果相比，使用 Discrimination-DL 进行的 COVID-19 判别准确率为 98.71%，而使用 Localization-DL 进行的定位准确率为 93.03%。这项工作代表了使用一种新颖的基于深度学习的 CAD 方案来高效、准确地区分 COVID-19 与 CAP 并以高精度检测定位并与放射科医生保持一致的可行性。