COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
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DOI：
10.1016/j.cmpb.2020.105532
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发表时间：
2020-10-01
影响因子：
6.1
通讯作者：
Costa, Yandre M. G.
中科院分区：
文献类型：
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作者：
Pereira, Rodolfo M.;Bertolini, Diego;Costa, Yandre M. G.
背景和目的：COVID-19可导致严重肺炎，估计对医疗保健系统有很大影响。早期诊断对于正确治疗至关重要，以便可能减轻医疗保健系统的压力。肺炎的标准影像诊断测试是胸部X线（CXR）和计算机断层扫描（CT）扫描。虽然CT扫描是黄金标准，但CXR仍然很有用，因为它更便宜，更快，更广泛。本研究旨在仅使用CXR图像识别由COVID-19引起的肺炎与其他类型以及健康的肺部。方法：为了实现目标，我们提出了一种考虑以下观点的分类方案：i）多类分类; ii）分层分类，因为肺炎可以被构造为层次结构。鉴于这一领域中的自然数据不平衡，我们还提出了在模式中使用重排序算法，以重新平衡类分布。我们观察到，纹理是CXR图像的主要视觉属性之一，我们的分类模式使用一些众所周知的纹理描述符和预训练的CNN模型提取特征。我们还探讨了早期和后期的融合技术的模式，以利用多个纹理描述符和基本分类器的强度在一次。为了评估这种方法，我们组成了一个名为RYDLS-20的数据库，其中包含由不同病原体引起的肺炎的CXR图像以及健康肺部的CXR图像。类别分布遵循真实世界的场景，其中一些病原体比其他病原体更常见。结果：在RYDLS-20中测试的所提出的方法使用多类方法实现了0.65的宏观平均F1-Score，并且在分层分类场景中用于COVID-19识别的F1-Score为0.89。据我们所知，本文获得的最高识别率是在三个以上类别的不平衡环境中对COVID-19识别获得的最佳标称率。我们还必须强调为这项任务提出的新的分层分类方法，该方法考虑了不同病原体引起的肺炎类型，并使我们获得了这里获得的最佳COVID-19识别率。(c)2020爱思唯尔B. V.保留所有权利。
Background and Objective: The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. Early diagnosis is crucial for correct treatment in order to possibly reduce the stress in the healthcare system. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. Although CT scan is the gold standard, CXR are still useful because it is cheaper, faster and more widespread. This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images.Methods: In order to achieve the objectives, we have proposed a classification schema considering the following perspectives: i) a multi-class classification; ii) hierarchical classification, since pneumonia can be structured as a hierarchy. Given the natural data imbalance in this domain, we also proposed the use of resampling algorithms in the schema in order to re-balance the classes distribution. We observed that, texture is one of the main visual attributes of CXR images, our classification schema extract features using some well-known texture descriptors and also using a pre-trained CNN model. We also explored early and late fusion techniques in the schema in order to leverage the strength of multiple texture descriptors and base classifiers at once. To evaluate the approach, we composed a database, named RYDLS-20, containing CXR images of pneumonia caused by different pathogens as well as CXR images of healthy lungs. The classes distribution follows a real-world scenario in which some pathogens are more common than others.Results: The proposed approach tested in RYDLS-20 achieved a macro-avg F1-Score of 0.65 using a multiclass approach and a F1-Score of 0.89 for the COVID-19 identification in the hierarchical classification scenario.Conclusions: As far as we know, the top identification rate obtained in this paper is the best nominal rate obtained for COVID-19 identification in an unbalanced environment with more than three classes. We must also highlight the novel proposed hierarchical classification approach for this task, which considers the types of pneumonia caused by the different pathogens and lead us to the best COVID-19 recognition rate obtained here. (c) 2020 Elsevier B.V. All rights reserved.