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The new coronavirus (known as COVID-19) was first identified in Wuhan andquickly spread worldwide, wreaking havoc on the economy and people's everydaylives. Fever, cough, sore throat, headache, exhaustion, muscular aches, anddifficulty breathing are all typical symptoms of COVID-19. A reliable detectiontechnique is needed to identify affected individuals and care for them in theearly stages of COVID-19 and reduce the virus's transmission. The mostaccessible method for COVID-19 identification is RT-PCR; however, due to itstime commitment and false-negative results, alternative options must be sought.Indeed, compared to RT-PCR, chest CT scans and chest X-ray images providesuperior results. Because of the scarcity and high cost of CT scan equipment,X-ray images are preferable for screening. In this paper, a pre-trainednetwork, DenseNet169, was employed to extract features from X-ray images.Features were chosen by a feature selection method (ANOVA) to reducecomputations and time complexity while overcoming the curse of dimensionalityto improve predictive accuracy. Finally, selected features were classified byXGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate theproposed method. This method reached 98.72% accuracy for two-classclassification (COVID-19, healthy) and 92% accuracy for three-classclassification (COVID-19, healthy, pneumonia).

A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images