论文标题
深浮标：使用深度转移学习从胸部X射线图像预测COVID-19
Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
论文作者
论文摘要
COVID-19大流行正在全球150多个国家造成重大爆发，对全球许多人的健康和生活产生严重影响。战斗COVID-19的关键步骤之一是能够尽早发现感染患者，并特别谨慎地发现感染患者。从射线照相和放射学图像中检测这种疾病可能是诊断患者的最快方法之一。一些早期研究表明，感染了Covid-19的患者的胸部射线函数中的特定异常。受早期作品的启发，我们研究了深度学习模型从其胸部X射线照相图像中检测Covid-19患者的应用。我们首先从公开可用的数据集中准备了5,000张胸部X射线的数据集。董事会认证的放射科医生鉴定出表现出COVID-19的疾病存在的图像。在2,000个放射图的子集上的转移学习用于训练四个流行的卷积神经网络，包括Resnet18，Resnet50，Squeezenet和Densenet-121，以在分析的经过分析的胸部X射线图像中鉴定Covid-19疾病。我们在其余3,000张图像上评估了这些模型，其中大多数网络的灵敏度率为98％（$ \ pm $ 3％），而特异性率约为90％。除了灵敏度和特异性率外，我们还介绍了每个模型的接收器操作特性（ROC）曲线，Precision-Recall曲线，平均预测和混淆矩阵。我们还使用一种技术来生成可能受到Covid-19感染的肺部区域的热图，并表明生成的热图包含我们董事会认证的放射线医生注释的大多数感染区域。尽管所达到的性能非常令人鼓舞，但需要在一组更大的Covid-19图像上进行进一步的分析，以更可靠地估计准确率。数据集，模型实现（在Pytorch中）和评估都可以在https://github.com/shervinmin/deepcovid.git上公开可用于研究社区。
The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest ways to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of deep learning models to detect COVID-19 patients from their chest radiography images. We first prepare a dataset of 5,000 Chest X-rays from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist. Transfer learning on a subset of 2,000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate of 98% ($\pm$ 3%), while having a specificity rate of around 90%. Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC) curve, precision-recall curve, average prediction, and confusion matrix of each model. We also used a technique to generate heatmaps of lung regions potentially infected by COVID-19 and show that the generated heatmaps contain most of the infected areas annotated by our board certified radiologist. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID-19 images, to have a more reliable estimation of accuracy rates. The dataset, model implementations (in PyTorch), and evaluations, are all made publicly available for research community at https://github.com/shervinmin/DeepCovid.git
