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Background and Purpose: Coronaviruses (CoV) are perilous viruses that maycause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East RespiratorySyndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) wasdiscovered as a novel disease pneumonia in the city of Wuhan, China at the endof 2019. Now, it becomes a Coronavirus outbreak around the world, the number ofinfected people and deaths are increasing rapidly every day according to theupdated reports of the World Health Organization (WHO). Therefore, the aim ofthis article is to introduce a new deep learning framework; namely COVIDX-Netto assist radiologists to automatically diagnose COVID-19 in X-ray images.Materials and Methods: Due to the lack of public COVID-19 datasets, the studyis validated on 50 Chest X-ray images with 25 confirmed positive COVID-19cases. The COVIDX-Net includes seven different architectures of deepconvolutional neural network models, such as modified Visual Geometry GroupNetwork (VGG19) and the second version of Google MobileNet. Each deep neuralnetwork model is able to analyze the normalized intensities of the X-ray imageto classify the patient status either negative or positive COVID-19 case.Results: Experiments and evaluation of the COVIDX-Net have been successfullydone based on 80-20% of X-ray images for the model training and testing phases,respectively. The VGG19 and Dense Convolutional Network (DenseNet) modelsshowed a good and similar performance of automated COVID-19 classification withf1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. Conclusions:This study demonstrated the useful application of deep learning models toclassify COVID-19 in X-ray images based on the proposed COVIDX-Net framework.Clinical studies are the next milestone of this research work.

COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images