论文标题
COVIDX-NET：深度学习分类器的框架，用于诊断X射线图像中的Covid-19
COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
论文作者
论文摘要
背景和目的：冠状病毒（COV）是可能导致严重急性呼吸综合征（SARS-COV），中东呼吸综合征（MERS-COV）的危险病毒。 2019年新颖的2019年冠状病毒疾病（Covid-19）被发现是一种新型疾病肺炎，在2019年底，中国武汉市。现在，根据世界卫生组织的更新报告，它每天都在迅速增加了世界各地的冠状病毒爆发，受感染者和死亡的数量在每天迅速增加。因此，本文的目的是介绍一个新的深度学习框架。即Covidx-NET可以帮助放射学家在X射线图像中自动诊断Covid-19。材料和方法：由于缺乏公共COVID-19数据集，该研究已在50个胸部X射线图像上进行了验证，其中25个已确认的Covid-19案例。 COVIDX-NET包括深卷积神经网络模型的七个不同架构，例如修改后的Visual Geometry Group Network（VGG19）和Google Mobilenet的第二版。每个深度神经网络模型都能够分析X射线图像的归一化强度，以对患者状态为阴性或阳性Covid-19情况。结果：基于80-20％的X射线图像，用于模型训练和测试阶段的80-20％的X射线图像成功完成了Covidx-NET的实验和评估。 VGG19和密集的卷积网络（Densenet）模型分别显示出自动化的COVID-19分类的良好和相似的性能，而正常和COVID-19的F1分别为0.89和0.91。结论：这项研究证明了基于拟议的Covidx-Net框架，深度学习模型在X射线图像中对Covid-19进行了有用的应用。临床研究是这项研究工作的下一个里程碑。
Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework; namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work.
