[论文解读] Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI

Anas Tahir, Yazan Qiblawey+6 more|arXiv (Cornell University)|May 23, 2020

COVID-19 diagnosis using AI参考文献 35被引用 23

一句话总结

本研究使用深度学习结合先进的图像预处理与神经网络技术，区分COVID-19、SARS和MERS的肺部CT扫描图像。使用三通道拼接输入的InceptionV3模型对COVID-19的敏感度达到99.5%，但MERS与COVID-19之间存在重叠特征，导致有限的误分类。

ABSTRACT

Novel Coronavirus disease (COVID-19) is an extremely contagious and quickly spreading Coronavirus disease. Severe Acute Respiratory Syndrome (SARS)-CoV, Middle East Respiratory Syndrome (MERS)-CoV outbreak in 2002 and 2011 and current COVID-19 pandemic all from the same family of Coronavirus. The fatality rate due to SARS and MERS were higher than COVID-19 however, the spread of those were limited to few countries while COVID-19 affected more than two-hundred countries of the world. In this work, authors used deep machine learning algorithms along with innovative image pre-processing techniques to distinguish COVID-19 images from SARS and MERS images. Several deep learning algorithms were trained, and tested and four outperforming algorithms were reported: SqueezeNet, ResNet18, Inceptionv3 and DenseNet201. Original, Contrast limited adaptive histogram equalized and complemented image were used individually and in concatenation as the inputs to the networks. It was observed that inceptionv3 outperforms all networks for 3-channel concatenation technique and provide an excellent sensitivity of 99.5%, 93.1% and 97% for classifying COVID-19, MERS and SARS images respectively. Investigating deep layer activation mapping of the correctly classified images and miss-classified images, it was observed that some overlapping features between COVID-19 and MERS images were identified by the deep layer network. Interestingly these features were present in MERS images and 10 out of 144 images were miss-classified as COVID while only one out of 423 COVID-19 images was miss-classified as MERS. None of the MERS images was miss-classified to SARS and only one COVID-19 image was miss-classified as SARS. Therefore, it can be summarized that SARS images are significantly different from MERS and COVID-19 in the eyes of AI while there are some overlapping feature available between MERS and COVID-19.

研究动机与目标

• 开发一种基于人工智能的方法，以区分COVID-19、SARS和MERS的肺部CT扫描图像。
• 利用深度学习研究这些冠状病毒相关疾病在视觉和特征层面的相似性与差异性。
• 评估多种深度学习架构在高敏感度和高特异性下对这些疾病的分类性能。
• 分析特征激活图，以理解MERS与COVID-19之间某些误分类现象的原因。

提出的方法

• 在肺部CT图像上训练并测试了多种深度学习模型：SqueezeNet、ResNet18、InceptionV3和DenseNet201。
• 应用了三种图像预处理技术：原始图像、对比度受限自适应直方图均衡化（CLAHE）以及补色图像。
• 将预处理后的图像进行拼接作为输入，以增强跨通道的特征表示。
• 通过测试集的敏感度和误分类分析来评估模型性能。
• 进行深层激活图映射，以解释模型决策过程，并识别疾病之间的共享特征。
• 对比所有三种疾病的分类结果，以评估模型的鲁棒性及特征重叠情况。

实验结果

研究问题

• RQ1 哪种深度学习模型在区分COVID-19、SARS和MERS肺部CT扫描图像方面表现最佳？
• RQ2 不同的图像预处理技术如何影响深度学习模型的分类性能？
• RQ3 MERS与COVID-19之间存在哪些视觉特征导致误分类？
• RQ4 在深度神经网络学习的特征空间中，SARS图像与MERS和COVID-19图像的差异有多大？
• RQ5 最佳性能模型对每种疾病类别的敏感度和特异性分别是多少？

主要发现

• 使用三通道拼接输入的InceptionV3模型对COVID-19图像的分类敏感度最高，达到99.5%。
• MERS图像与COVID-19存在显著的特征重叠，导致144张MERS图像中有10张被误分类为COVID-19。
• 在423张COVID-19图像中仅有一张被误分类为MERS，表明该类别具有很强的模型区分能力。
• 没有MERS图像被误分类为SARS，且仅有一张COVID-19图像被误分类为SARS，表明SARS与其他两类疾病具有高度可分性。
• 深层激活图显示，MERS与COVID-19之间的重叠特征是导致观察到误分类现象的主要原因。
• 在深度神经网络学习的表征中，SARS图像在视觉和特征层面均与MERS和COVID-19明显不同。