  The novel 2019 coronavirus disease (COVID-19) has infected over 65 million
people worldwide as of December 4, 2020, pushing the world to the brink of
social and economic collapse. With cases rising rapidly, deep learning has
emerged as a promising diagnosis technique. However, identifying the most
accurate models to characterize COVID-19 patients is challenging because
comparing results obtained with different types of data and acquisition
processes is non-trivial. In this paper, we evaluated and compared 40 different
convolutional neural network architectures for COVID-19 diagnosis, serving as
the first to consider the EfficientNet family for COVID-19 diagnosis.
EfficientNet-B5 is identified as the best model with an accuracy of
0.9931+/-0.0021, F1 score of 0.9931+/-0.0020, sensitivity of 0.9952+/-0.0020,
and specificity of 0.9912+/-0.0048. Intermediate activation maps and
Gradient-weighted Class Activation Mappings offer human-interpretable evidence
of the model's perception of ground-class opacities and consolidations, hinting
towards a promising use-case of artificial intelligence-assisted radiology
tools.
