论文标题
COVID-19肺炎，非旋转-19肺炎和健康的胸部X射线图像之间的自动分类：数据增强方法的组合
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods
论文作者
论文摘要
目的：这项研究旨在开发和验证计算机辅助诊断（CXDX）系统，以在19009肺炎，非旋转-19肺炎和健康的胸部X射线（CXR）图像之间进行分类。 材料和方法：从两个公共数据集中获得了1248张CXR图像，其中包括215、533和500 CXR图像，19009年肺炎患者，非舒张性肺炎患者和健康样本。所提出的CADX系统利用VGG16作为预训练的模型，并将常规方法和混音作为数据增强方法的组合。将其他类型的预训练模型与基于VGG16的模型进行了比较。还评估了单型或没有数据增强方法。在构建和评估CADX系统时，使用培训/验证/测试集的分裂。用125个CXR图像评估了三类精度的测试集。 结果：CAD系统的三类准确性在Covid-19-19肺炎，非旋转-19肺炎和健康之间为83.6％。 Covid-19肺炎的敏感性超过90％。常规方法和混合的组合比单类型或无数据增强方法更有用。 结论：这项研究能够为3类分类创建准确的CADX系统。我们的CADX系统的源代码可作为COVID-19研究的开源。
Purpose: This study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray (CXR) images. Materials and Methods: From two public datasets, 1248 CXR images were obtained, which included 215, 533, and 500 CXR images of COVID-19 pneumonia patients, non-COVID-19 pneumonia patients, and the healthy samples. The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods. Other types of pre-trained models were compared with the VGG16-based model. Single type or no data augmentation methods were also evaluated. Splitting of training/validation/test sets was used when building and evaluating the CADx system. Three-category accuracy was evaluated for test set with 125 CXR images. Results: The three-category accuracy of the CAD system was 83.6% between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy. Sensitivity for COVID-19 pneumonia was more than 90%. The combination of conventional method and mixup was more useful than single type or no data augmentation method. Conclusion: This study was able to create an accurate CADx system for the 3-category classification. Source code of our CADx system is available as open source for COVID-19 research.
