[论文解读] Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images

Anas Tahir, Yazan Qiblawey+7 more|arXiv (Cornell University)|May 23, 2020

COVID-19 diagnosis using AI被引用 5

一句话总结

本文提出了一种基于深度学习的框架，通过两阶段方法（先使用U-Net进行肺部分割，再利用预训练的CNN进行分类）对胸部X光片中的COVID-19、MERS和SARS进行分类。该方法在检测COVID-19时表现出高达99.5%的敏感性，表明人工智能能够可靠地识别这些冠状病毒感染的独特放射影像特征，即使在临床诊断具有挑战性的情况下亦如此。

ABSTRACT

Novel Coronavirus disease (COVID-19) is an extremely contagious and quickly spreading Coronavirus infestation. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep Convolutional Neural Networks (CNNs). A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several Deep Learning classifiers were trained and tested; four outperforming algorithms were reported. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. All networks showed high COVID-19 detection sensitivity (>96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.

研究动机与目标

• 开发一种可靠的深度学习系统，利用胸部X光片区分COVID-19、MERS和SARS。
• 通过人工智能检测细微的放射影像特征，解决早期冠状病毒感染诊断准确率低的问题。
• 创建一个新颖且公开可用的数据集（QU-COVID-family），包含423例COVID-19、144例MERS和134例SARS的X光片，以供研究使用。
• 通过在多阶段深度学习流程中结合全图输入与分割后的肺部输入，提升分类性能。

提出的方法

• 构建了一个名为QU-COVID-family的自定义数据集，包含423例COVID-19、144例MERS和134例SARS的胸部X光片。
• 采用基于U-Net的卷积神经网络对X光片中的肺部区域进行分割，以隔离感兴趣区域，从而提升分类性能。
• 对预训练的InceptionV3及其他CNN架构进行微调，用于对原始图像和分割后图像的分类。
• 评估了两种识别方案：普通X光片分类与分割后X光片分类，均采用三通道输入表示。
• 应用Score-CAM可视化技术以解释模型决策，并验证模型对与疾病相关的肺部区域的关注。
• 在不同输入类型和网络架构下训练并比较了多个深度学习模型，以识别性能最优的配置。

实验结果

研究问题

• RQ1 深度学习模型能否仅通过胸部X光片可靠地区分COVID-19、MERS和SARS？
• RQ2 与使用完整图像相比，从X光片中分割肺部区域是否能提升冠状病毒类型分类的准确率？
• RQ3 在所提出的QU-COVID-family数据集中，哪种预训练的CNN架构在分类这三种冠状病毒相关疾病时表现最佳？
• RQ4 模型在多大程度上能够检测到每种病毒的独特放射影像特征，特别是对COVID-19？
• RQ5 深度学习模型的决策是否具有可解释性，且是否聚焦于临床上相关的肺部区域？

主要发现

• 在使用三通道输入方案的普通胸部X光片中，InceptionV3对COVID-19分类的敏感度达到最高，为99.5%。
• 在分割后的肺部图像中，InceptionV3对COVID-19的敏感度为96.94%，对MERS为79.68%，对SARS为90.26%。
• 所有测试的神经网络在使用分割后的肺部图像时，对COVID-19检测的敏感度均超过96%，表明该条件下的模型表现具有一致性。
• 分割后X光片分类方案提升了模型的鲁棒性与可解释性，Score-CAM可视化结果突出了受疾病影响的肺部区域。
• QU-COVID-family数据集为未来利用医学影像进行冠状病毒分类研究提供了宝贵的基准。
• 本研究证明，深度学习能够检测到临床医生可能难以一致识别的COVID-19细微且独特的放射影像特征。