  In recent months the world has been surprised by the rapid advance of
COVID-19. In order to face this disease and minimize its socio-economic
impacts, in addition to surveillance and treatment, diagnosis is a crucial
procedure. However, the realization of this is hampered by the delay and the
limited access to laboratory tests, demanding new strategies to carry out case
triage. In this scenario, deep learning models are being proposed as a possible
option to assist the diagnostic process based on chest X-ray and computed
tomography images. Therefore, this research aims to automate the process of
detecting COVID-19 cases from chest images, using convolutional neural networks
(CNN) through deep learning techniques. The results can contribute to expand
access to other forms of detection of COVID-19 and to speed up the process of
identifying this disease. All databases used, the codes built, and the results
obtained from the models' training are available for open access. This action
facilitates the involvement of other researchers in enhancing these models
since this can contribute to the improvement of results and, consequently, the
progress in confronting COVID-19.
