Hybrid approach for COVID-19 detection from chest radiography

Automatic and rapid screening of COVID-19
from the chest X-ray and Computerized Tomography (CT)
images has become an urgent need in this pandemic situation
of SARS-CoV-2 worldwide. However, accurate and reliable
screening of patients is a massive challenge due to the
discrepancy between COVID-19 and other viral pneumonia in
both X-ray and CT images. Several models were introduced,
but always there was a glitch that might be due to the use of a
single classifier, and this reduces their accuracy. In this paper,
we study the use of multi-classifiers and show their effect on
different models working on X-ray and CT images. We
perform a comparison study to show the high impact of
ensemble stacking approach on top performer CNN models
that recorded the highest detection accuracy in image detection
and classification: COVID-Net, VGG16, ResNet, Bayesian,
DenseNet, and DarkNet. We presented multi-classifiers instead
of a single classifier stacked in an ensemble stacking approach
for the diagnosis of the COVID19 from the Chest CT and Xray images. We provide a quantitative evaluation of the
proposed ensemble stacking approach on two types of datasets:
X-ray images and CT images datasets, with percentages
reaching 99%.