  Background and Purpose: Coronaviruses (CoV) are perilous viruses that may
cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory
Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was
discovered as a novel disease pneumonia in the city of Wuhan, China at the end
of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of
infected people and deaths are increasing rapidly every day according to the
updated reports of the World Health Organization (WHO). Therefore, the aim of
this article is to introduce a new deep learning framework; namely COVIDX-Net
to assist radiologists to automatically diagnose COVID-19 in X-ray images.
Materials and Methods: Due to the lack of public COVID-19 datasets, the study
is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19
cases. The COVIDX-Net includes seven different architectures of deep
convolutional neural network models, such as modified Visual Geometry Group
Network (VGG19) and the second version of Google MobileNet. Each deep neural
network model is able to analyze the normalized intensities of the X-ray image
to classify the patient status either negative or positive COVID-19 case.
Results: Experiments and evaluation of the COVIDX-Net have been successfully
done based on 80-20% of X-ray images for the model training and testing phases,
respectively. The VGG19 and Dense Convolutional Network (DenseNet) models
showed a good and similar performance of automated COVID-19 classification with
f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. Conclusions:
This study demonstrated the useful application of deep learning models to
classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework.
Clinical studies are the next milestone of this research work.
