The COVID-19 pandemic is undoubtedly one of the biggest public health crises our society has ever faced in recent history. One of the main complications caused by COVID-19 is pneumonia, which is diagnosed using imaging exams, such as chest X-ray (CXR) and computed tomography (CT) scan. The CT scan is more precise than the CXR. However, CXR is suitable in particular situations because it is cheaper, faster, more widespread, and exposes the patient to less radiation. This study aims to demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image decisively contribute to its identification. We performed the lung segmentation using a U-Net CNN architecture, and the classification using three well-known CNN architectures: VGG, ResNet, and Inception. To estimate the impact of lung segmentation, we applied some Explainable Artificial Intelligence (XAI) techniques, specifically LIME and Grad-CAM. To empirically evaluate our approach, we composed a database with three classes: lung opacity (pneumonia), COVID-19, and normal. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented lung achieved an F1-Score of 0.88 for the multi-class setup and 0.83 for COVID-19 identification. Further testing and XAI techniques suggest that segmented CXR images represent a much more realistic and less biased performance. To the best of our knowledge, no other work tried to estimate the impact of lung segmentation in COVID-19 identification using comprehensive XAI techniques.

翻译：COVID-19大流行无疑是我们社会在近代史上所面临的最大的公共卫生危机之一。COVID-19大流行无疑是我们社会在近期历史上所面临的最大公共卫生危机之一。COVID-19大流行造成的主要并发症之一是肺炎,肺炎是通过X光胸X光(CXR)和计算断层(CT)扫描等成像检查诊断出来的。CT扫描比CXR更精确。然而,CXR在特定情况下是合适的,因为它比较便宜、更快、更加广泛,使病人受到较少的辐射。这项研究旨在展示使用CXR图像进行COVID-19识别的肺部分离作用,并评估图像中哪些内容决定性地有助于其识别。我们使用U-NetCNN(CX)结构以及三个著名的CNN结构(VG、ResNet和Inception)进行了肺部切分解。但是为了估计肺部分解的影响,我们采用了一些可解释的CAI(X)技术,特别是LME和GRAD-CAM。我们用实验性评估了我们的方法,我们用三个类数据库:肺部不透明(肺部)、CNE-D-19AD-19分层(CAVID) 和正常分解法的分解方法,我们用一个小的分解方法,我们用的是CARC-C-C-C-C-C-C-C-C-C-C-C-C-C-C-38-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C