  The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed
the world. According to the World Health Organization (WHO), there have been
more than 100 million confirmed cases of COVID-19, including more than 2.4
million deaths. It is extremely important the early detection of the disease,
and the use of medical imaging such as chest X-ray (CXR) and chest Computed
Tomography (CCT) have proved to be an excellent solution. However, this process
requires clinicians to do it within a manual and time-consuming task, which is
not ideal when trying to speed up the diagnosis. In this work, we propose an
ensemble classifier based on probabilistic Support Vector Machine (SVM) in
order to identify pneumonia patterns while providing information about the
reliability of the classification. Specifically, each CCT scan is divided into
cubic patches and features contained in each one of them are extracted by
applying kernel PCA. The use of base classifiers within an ensemble allows our
system to identify the pneumonia patterns regardless of their size or location.
Decisions of each individual patch are then combined into a global one
according to the reliability of each individual classification: the lower the
uncertainty, the higher the contribution. Performance is evaluated in a real
scenario, yielding an accuracy of 97.86%. The large performance obtained and
the simplicity of the system (use of deep learning in CCT images would result
in a huge computational cost) evidence the applicability of our proposal in a
real-world environment.
