  The 2019 novel coronavirus disease (COVID-19), with a starting point in
China, has spread rapidly among people living in other countries, and is
approaching approximately 34,986,502 cases worldwide according to the
statistics of European Centre for Disease Prevention and Control. There are a
limited number of COVID-19 test kits available in hospitals due to the
increasing cases daily. Therefore, it is necessary to implement an automatic
detection system as a quick alternative diagnosis option to prevent COVID-19
spreading among people. In this study, five pre-trained convolutional neural
network based models (ResNet50, ResNet101, ResNet152, InceptionV3 and
Inception-ResNetV2) have been proposed for the detection of coronavirus
pneumonia infected patient using chest X-ray radiographs. We have implemented
three different binary classifications with four classes (COVID-19, normal
(healthy), viral pneumonia and bacterial pneumonia) by using 5-fold cross
validation. Considering the performance results obtained, it has seen that the
pre-trained ResNet50 model provides the highest classification performance
(96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy
for Dataset-3) among other four used models.
