  The ongoing crisis of the COVID-19 (Coronavirus disease 2019) pandemic has
changed the world. According to the World Health Organization (WHO), 4 million
people have died due to this disease, whereas there have been more than 180
million confirmed cases of COVID-19. The collapse of the health system in many
countries has demonstrated the need of developing tools to automatize the
diagnosis of the disease from medical imaging. Previous studies have used deep
learning for this purpose. However, the performance of this alternative highly
depends on the size of the dataset employed for training the algorithm. In this
work, we propose a classification framework based on sparse coding in order to
identify the pneumonia patterns associated with different pathologies.
Specifically, each chest X-ray (CXR) image is partitioned into different tiles.
The most relevant features extracted from PCA are then used to build the
dictionary within the sparse coding procedure. Once images are transformed and
reconstructed from the elements of the dictionary, classification is performed
from the reconstruction errors of individual patches associated with each
image. Performance is evaluated in a real scenario where simultaneously
differentiation between four different pathologies: control vs bacterial
pneumonia vs viral pneumonia vs COVID-19. The accuracy when identifying the
presence of pneumonia is 93.85%, whereas 88.11% is obtained in the 4-class
classification context. The excellent results and the pioneering use of sparse
coding in this scenario evidence the applicability of this approach as an aid
for clinicians in a real-world environment.
