Classification Of X-Ray Of Covid-19, Normal, And Pneumonia Affected Patient Using A Deep Learning Model

Abstract

SARS CoV-2, the cause of the corona virus illness 2019 (COVID-19), may be found using chest X-ray scans,
and this discovery could save the lives of both patients and medical professionals. This is especially more
important in nations where it is impossible to buy laboratory kits for testing. In this study, we sought to
demonstrate the use of deep learning to chest X-ray images for very accurate COVID-19 identification. Publicly
available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the
experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight
experiments were performed using convolutional neural networks, 10 experiments were performed using five
machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks
for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the
performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean
specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the
curve scores of 96.51% are achieved. A convolutional neural network is able to identify COVID-19 in a small
number of unbalanced chest X-ray pictures without pre-processing and with minimal layers.