  Clinicians in the frontline need to assess quickly whether a patient with
symptoms indeed has COVID-19 or not. The difficulty of this task is exacerbated
in low resource settings that may not have access to biotechnology tests.
Furthermore, Tuberculosis (TB) remains a major health problem in several low-
and middle-income countries and its common symptoms include fever, cough and
tiredness, similarly to COVID-19. In order to help in the detection of
COVID-19, we propose the extraction of deep features (DF) from chest X-ray
images, a technology available in most hospitals, and their subsequent
classification using machine learning methods that do not require large
computational resources. We compiled a five-class dataset of X-ray chest images
including a balanced number of COVID-19, viral pneumonia, bacterial pneumonia,
TB, and healthy cases. We compared the performance of pipelines combining 14
individual state-of-the-art pre-trained deep networks for DF extraction with
traditional machine learning classifiers. A pipeline consisting of ResNet-50
for DF computation and ensemble of subspace discriminant classifier was the
best performer in the classification of the five classes, achieving a detection
accuracy of 91.6+ 2.6% (accuracy + 95% Confidence Interval). Furthermore, the
same pipeline achieved accuracies of 98.6+1.4% and 99.9+0.5% in simpler
three-class and two-class classification problems focused on distinguishing
COVID-19, TB and healthy cases; and COVID-19 and healthy images, respectively.
The pipeline was computationally efficient requiring just 0.19 second to
extract DF per X-ray image and 2 minutes for training a traditional classifier
with more than 2000 images on a CPU machine. The results suggest the potential
benefits of using our pipeline in the detection of COVID-19, particularly in
resource-limited settings and it can run with limited computational resources.
