  In late 2019 and after COVID-19 pandemic in the world, many researchers and
scholars have tried to provide methods for detection of COVID-19 cases.
Accordingly, this study focused on identifying COVID-19 cases from chest X-ray
images. In this paper, a novel approach to diagnosing coronavirus disease from
X-ray images was proposed. In the proposed method, DenseNet169 deep neural
network was used to extract the features of X-ray images taken from the
patients' chest and the extracted features were then given as input to the
Extreme Gradient Boosting (XGBoost) algorithm so that it could perform the
classification task. Evaluation of the proposed approach and its comparison
with the methods presented in recent years revealed that the proposed method
was more accurate and faster than the existing ones and had an acceptable
performance in detection of COVID-19 cases from X-ray images.
