  COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging
exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread,
and uses less radiation. Here, we demonstrate the impact of lung segmentation
in COVID-19 identification using CXR images and evaluate which contents of the
image influenced the most. Semantic segmentation was performed using a U-Net
CNN architecture, and the classification using three CNN architectures (VGG,
ResNet, and Inception). Explainable Artificial Intelligence techniques were
employed to estimate the impact of segmentation. A three-classes database was
composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the
impact of creating a CXR image database from different sources, and the
COVID-19 generalization from one source to another. The segmentation achieved a
Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification
using segmented images achieved an F1-Score of 0.88 for the multi-class setup,
and 0.83 for COVID-19 identification. In the cross-dataset scenario, we
obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for
COVID-19 identification using segmented images. Experiments support the
conclusion that even after segmentation, there is a strong bias introduced by
underlying factors from different sources.
