  This technical report proposes the use of a deep convolutional neural network
as a preliminary diagnostic method in the analysis of chest computed tomography
images from patients with symptoms of Severe Acute Respiratory Syndrome (SARS)
and suspected COVID-19 disease, especially on occasions when the delay of the
RT-PCR result and the absence of urgent care could result in serious temporary,
long-term, or permanent health damage. The model was trained on 83,391 images,
validated on 15,297, and tested on 22,185 figures, achieving an F1-Score of
98%, 97.59% in Cohen's Kappa, 98.4% in Accuracy, and 5.09% in Loss. Attesting a
highly accurate automated classification and providing results in less time
than the current gold-standard exam, Real-Time reverse-transcriptase Polymerase
Chain Reaction (RT-PCR).
