论文标题
使用预处理算法提高CNN的性能，以预测COVID-19的可能性。
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
论文作者
论文摘要
随着全球冠状病毒疾病（Covid-19）的迅速传播，胸部X射线射线照相也已用于检测COVID-19受感染的肺炎，并评估其严重程度或监测其在医院的预后，因为其低成本，低辐射剂量和广泛的可及性。但是，如何更准确，有效地检测到Covid-19感染的肺炎，并将其与其他社区获得的肺炎区分开来仍然是一个挑战。为了应对这一挑战，我们在这项研究中开发和测试了新的计算机辅助诊断（CAD）方案。 It includes several image pre-processing algorithms to remove diaphragms, normalize image contrast-to-noise ratio, and generate three input images, then links to a transfer learning based convolutional neural network (a VGG16 based CNN model) to classify chest X-ray images into three classes of COVID-19 infected pneumonia, other community-acquired pneumonia and normal (non-pneumonia) cases.为此，使用了8,474张胸部X射线图像的公开数据集，其中包括415个确认的COVID-COVID-19受感染的肺炎，5,179个社区获得的肺炎和2,880例非pneumonia病例。该数据集分为两个子集，每个子集中有90％和10％的图像，以训练和测试基于CNN的CAD方案。测试结果在分类三个类别和98.6％的准确性方面达到了94.0％的总体精度，以检测COVID-19受感染病例。因此，该研究证明了开发胸部X射线图像的CAD方案的可行性，并为放射学家提供了有用的决策支持工具，以检测和诊断Covid-19受感染的肺炎。
As the rapid spread of coronavirus disease (COVID-19) worldwide, chest X-ray radiography has also been used to detect COVID-19 infected pneumonia and assess its severity or monitor its prognosis in the hospitals due to its low cost, low radiation dose, and wide accessibility. However, how to more accurately and efficiently detect COVID-19 infected pneumonia and distinguish it from other community-acquired pneumonia remains a challenge. In order to address this challenge, we in this study develop and test a new computer-aided diagnosis (CAD) scheme. It includes several image pre-processing algorithms to remove diaphragms, normalize image contrast-to-noise ratio, and generate three input images, then links to a transfer learning based convolutional neural network (a VGG16 based CNN model) to classify chest X-ray images into three classes of COVID-19 infected pneumonia, other community-acquired pneumonia and normal (non-pneumonia) cases. To this purpose, a publicly available dataset of 8,474 chest X-ray images is used, which includes 415 confirmed COVID-19 infected pneumonia, 5,179 community-acquired pneumonia, and 2,880 non-pneumonia cases. The dataset is divided into two subsets with 90% and 10% of images in each subset to train and test the CNN-based CAD scheme. The testing results achieve 94.0% of overall accuracy in classifying three classes and 98.6% accuracy in detecting Covid-19 infected cases. Thus, the study demonstrates the feasibility of developing a CAD scheme of chest X-ray images and providing radiologists useful decision-making supporting tools in detecting and diagnosis of COVID-19 infected pneumonia.
