论文标题
用于使用胸部X射线检测COVID-19的级联网络
A cascade network for Detecting COVID-19 using chest x-rays
论文作者
论文摘要
新型冠状病毒引起的肺炎在全球范围内蔓延，对世界医疗资源，预防和控制措施构成了前所未有的挑战。 Covid-19不仅攻击肺部，使呼吸困难和威胁生命，而且还具有可能的后遗症。目前，需要通过逆转录聚合酶链反应（RT-PCR）来实现COVID-19的检测。但是，许多国家处于流行病的爆发期，医疗资源非常有限。他们无法提供足够数量的基因序列检测，并且许多患者可能不会及时分离和治疗。鉴于这种情况，我们研究了对胸部X光片和提议的级联 - 丝网的深度学习能力，并用Seme-Resnet50和Seme-Densenet169级联。级联 - 精液的两个级联网络都采用大型输入大小和SETRUCTURE，并使用MOEX和直方图均衡来增强数据。我们首先使用seme-resnet50筛选胸部X射线，并诊断出三类：正常，细菌和病毒性肺炎。然后，我们使用seme-densenet169进行病毒肺炎的细粒分类，并确定它是否是由covid-19引起的。为了排除非病理特征对网络的影响，我们在训练SEME-DENSENET169期间使用U-NET进行了预处理。结果表明，我们的网络在确定肺炎感染的类型中的准确度为85.6 \％，在Covid-19的细粒度分类中，确定了97.1 \％。我们使用Grad-CAM根据模型可视化判断，并帮助医生在验证效果时了解胸部X光片。
The worldwide spread of pneumonia caused by a novel coronavirus poses an unprecedented challenge to the world's medical resources and prevention and control measures. Covid-19 attacks not only the lungs, making it difficult to breathe and life-threatening, but also the heart, kidneys, brain and other vital organs of the body, with possible sequela. At present, the detection of COVID-19 needs to be realized by the reverse transcription-polymerase Chain Reaction (RT-PCR). However, many countries are in the outbreak period of the epidemic, and the medical resources are very limited. They cannot provide sufficient numbers of gene sequence detection, and many patients may not be isolated and treated in time. Given this situation, we researched the analytical and diagnostic capabilities of deep learning on chest radiographs and proposed Cascade-SEMEnet which is cascaded with SEME-ResNet50 and SEME-DenseNet169. The two cascade networks of Cascade - SEMEnet both adopt large input sizes and SE-Structure and use MoEx and histogram equalization to enhance the data. We first used SEME-ResNet50 to screen chest X-ray and diagnosed three classes: normal, bacterial, and viral pneumonia. Then we used SEME-DenseNet169 for fine-grained classification of viral pneumonia and determined if it is caused by COVID-19. To exclude the influence of non-pathological features on the network, we preprocessed the data with U-Net during the training of SEME-DenseNet169. The results showed that our network achieved an accuracy of 85.6\% in determining the type of pneumonia infection and 97.1\% in the fine-grained classification of COVID-19. We used Grad-CAM to visualize the judgment based on the model and help doctors understand the chest radiograph while verifying the effectivene.
