  COVID-19 quickly became a global pandemic after only four months of its first
detection. It is crucial to detect this disease as soon as possible to decrease
its spread. The use of chest X-ray (CXR) images became an effective screening
strategy, complementary to the reverse transcription-polymerase chain reaction
(RT-PCR). Convolutional neural networks (CNNs) are often used for automatic
image classification and they can be very useful in CXR diagnostics. In this
paper, 21 different CNN architectures are tested and compared in the task of
identifying COVID-19 in CXR images. They were applied to the COVIDx8B dataset,
a large COVID-19 dataset with 16,352 CXR images coming from patients of at
least 51 countries. Ensembles of CNNs were also employed and they showed better
efficacy than individual instances. The best individual CNN instance results
were achieved by DenseNet169, with an accuracy of 98.15% and an F1 score of
98.12%. These were further increased to 99.25% and 99.24%, respectively,
through an ensemble with five instances of DenseNet169. These results are
higher than those obtained in recent works using the same dataset.
