  Purpose: We present image classifiers based on Dense Convolutional Networks
and transfer learning to classify chest X-ray images according to three labels:
COVID-19, pneumonia and normal.
  Methods: We fine-tuned neural networks pretrained on ImageNet and applied a
twice transfer learning approach, using NIH ChestX-ray14 dataset as an
intermediate step. We also suggested a novelty called output neuron keeping,
which changes the twice transfer learning technique. In order to clarify the
modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to
generate heatmaps.
  Results: We were able to reach test accuracy of 100% on our test dataset.
Twice transfer learning and output neuron keeping showed promising results
improving performances, mainly in the beginning of the training process.
Although LRP revealed that words on the X-rays can influence the networks'
predictions, we discovered this had only a very small effect on accuracy.
  Conclusion: Although clinical studies and larger datasets are still needed to
further ensure good generalization, the state-of-the-art performances we
achieved show that, with the help of artificial intelligence, chest X-rays can
become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps
generated by LRP improve the interpretability of the deep neural networks and
indicate an analytical path for future research on diagnosis. Twice transfer
learning with output neuron keeping improved performances.
