  Respiratory diseases kill million of people each year. Diagnosis of these
pathologies is a manual, time-consuming process that has inter and
intra-observer variability, delaying diagnosis and treatment. The recent
COVID-19 pandemic has demonstrated the need of developing systems to automatize
the diagnosis of pneumonia, whilst Convolutional Neural Network (CNNs) have
proved to be an excellent option for the automatic classification of medical
images. However, given the need of providing a confidence classification in
this context it is crucial to quantify the reliability of the model's
predictions. In this work, we propose a multi-level ensemble classification
system based on a Bayesian Deep Learning approach in order to maximize
performance while quantifying the uncertainty of each classification decision.
This tool combines the information extracted from different architectures by
weighting their results according to the uncertainty of their predictions.
Performance of the Bayesian network is evaluated in a real scenario where
simultaneously differentiating between four different pathologies: control vs
bacterial pneumonia vs viral pneumonia vs COVID-19 pneumonia. A three-level
decision tree is employed to divide the 4-class classification into three
binary classifications, yielding an accuracy of 98.06% and overcoming the
results obtained by recent literature. The reduced preprocessing needed for
obtaining this high performance, in addition to the information provided about
the reliability of the predictions evidence the applicability of the system to
be used as an aid for clinicians.
