  The new coronavirus (known as COVID-19) was first identified in Wuhan and
quickly spread worldwide, wreaking havoc on the economy and people's everyday
lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and
difficulty breathing are all typical symptoms of COVID-19. A reliable detection
technique is needed to identify affected individuals and care for them in the
early stages of COVID-19 and reduce the virus's transmission. The most
accessible method for COVID-19 identification is RT-PCR; however, due to its
time commitment and false-negative results, alternative options must be sought.
Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide
superior results. Because of the scarcity and high cost of CT scan equipment,
X-ray images are preferable for screening. In this paper, a pre-trained
network, DenseNet169, was employed to extract features from X-ray images.
Features were chosen by a feature selection method (ANOVA) to reduce
computations and time complexity while overcoming the curse of dimensionality
to improve predictive accuracy. Finally, selected features were classified by
XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the
proposed method. This method reached 98.72% accuracy for two-class
classification (COVID-19, healthy) and 92% accuracy for three-class
classification (COVID-19, healthy, pneumonia).
