###### Abstract  
The coronavirus pandemic started in Wuhan, China in December 2019, and put millions of people in a difficult situation. This fatal virus spread to over 227 countries and the number of infected patients increased to over 400 million cases, causing over 6 million deaths worldwide. Due to the serious consequence of this virus, it is necessary to develop a detection method that can respond quickly to prevent the spreading of COVID-19. Using chest X-ray images to detect COVID-19 is one of the promising techniques; however, with a large number of COVID-19 infected cases every day, the number of radiologists available to diagnose the chest X-ray images is not sufficient. We must have a computer aid system that helps doctors instantly and automatically determine COVID-19 cases. Recently, with the emergence of deep learning methods applied for medical and biomedical uses, using convolutional neural net and transformer applications for chest X-ray images can be a supplement for COVID-19 testing. In this paper, we attempt to classify three types of chest X-ray, which are normal, pneumonia, and COVID-19 using deep learning methods on a customized dataset. We also carry out an experiment on the COVID-19 severity assessment task using a tailored dataset. Five deep learning models were obtained to conduct our experiments: DenseNet121, ResNet50, InceptionNet, Swin Transformer, and Hybrid EfficientNet-DOLG neural networks. The results indicated that chest X-ray and deep learning could be reliable methods for supporting doctors in COVID-19 identification and severity assessment tasks. RT-PCR; coronavirus; convolutional neural network; transformer; chest X-ray; severity assessment +
Footnote †: journal: Medical  
false positive results with the RT-PCR test, which indicates the poor sensitivity and highly variable results of the method [9; 10; 11]. One of the recent emerging screening methods for COVID-19 is thoracic imaging analysis, which can be applied in early COVID-19 detection [12; 13; 14]. These chest X-ray images are obtained and analyzed by radiologists to find visual indicators related to SARS-CoV-2 viral infection. Former studies prove that COVID-19 causes abnormal areas that could be visible in chest X-rays; this hypothesis could be a strong suggestion to use chest X-rays as the initial step for COVID-19 monitoring [15; 16; 17]. Some of the most cutting-edge benefits when using chest X-rays for COVID-19 diagnosis include the following:  
* Chest X-ray diagnosis allows us to have rapid COVID-19 classification and could be carried out in parallel with RT-PCR testing to deal with high volumes of patients. * Chest X-ray images could be obtained in many clinical sites and are readily available in most health care centers. * The portable chest X-ray imaging system helps doctors to isolate the image capturing process from other people such that it can reduce the risk of spreading COVID-19. Despite chest X-ray diagnosis having many advantages, it still faces some obstacles due to some idiosyncratic characteristics of the new pandemic disease. The most cumbersome obstacle is the lack of experienced radiologists and also the error-prone human visual indicators. Computer aid design diagnosis can help radiologists to have faster and more accurate COVID-19 diagnosis as a crucial adjunct to reduce workload and enhance patient safety [18; 19; 20]. Recently, with the emergence of deep learning, many studies have been conducted to analyze the potential of chest X-ray and CT images for COVID-19 detection, especially to apply deep learning for automatic COVID-19 detection [21; 22; 23; 24; 25]. Deep learning for COVID-19 detection could help solve the problem of a lack of radiology specialists and also produce reliable performance [26; 27; 28]. However, almost all developed artificial intelligence (AI) systems are not open and are not available for the research community to access resources. We do not have much open-source coding or datasets available to conduct thorough research on the subject.