  The Corona Virus (COVID-19) is an internationalpandemic that has quickly
propagated throughout the world. The application of deep learning for image
classification of chest X-ray images of Covid-19 patients, could become a novel
pre-diagnostic detection methodology. However, deep learning architectures
require large labelled datasets. This is often a limitation when the subject of
research is relatively new as in the case of the virus outbreak, where dealing
with small labelled datasets is a challenge. Moreover, in the context of a new
highly infectious disease, the datasets are also highly imbalanced,with few
observations from positive cases of the new disease. In this work we evaluate
the performance of the semi-supervised deep learning architecture known as
MixMatch using a very limited number of labelled observations and highly
imbalanced labelled dataset. We propose a simple approach for correcting data
imbalance, re-weight each observationin the loss function, giving a higher
weight to the observationscorresponding to the under-represented class. For
unlabelled observations, we propose the usage of the pseudo and augmentedlabels
calculated by MixMatch to choose the appropriate weight. The MixMatch method
combined with the proposed pseudo-label based balance correction improved
classification accuracy by up to 10%, with respect to the non balanced MixMatch
algorithm, with statistical significance. We tested our proposed approach with
several available datasets using 10, 15 and 20 labelledobservations.
Additionally, a new dataset is included among thetested datasets, composed of
chest X-ray images of Costa Rican adult patients
