# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 1 Introduction  
The coronavirus (COVID-19) is possibly the biggest human threat of the twenty-first century. Since the disease outbreak in December 2019, the COVID-19 pandemic has become a worldwide health problem. According to the World Health Organization (WHO) statistics, in January 2022, there were more than 365 million confirmed COVID-19 cases, resulting in approximately 5.5 million deaths [1]. The COVID-19 pandemic disease is caused by severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2. COVID-19 belongs to the family of viruses which cause cold-related diseases, such as Middle East respiratory syndrome (MERS-CoV) and extreme acute respiratory syndrome [2]. The emergence of disease transmission and the rise in mortality in a number of countries has necessitated the protection of health services and the community from COVID-19 spread. Thus, remote disease monitoring, including early diagnosis and quarantine, and follow-up is of immense importance. Everyday, thousands of cases have been reported in many countries around the world, which lead some governments to implement lockdown measures to contain the virus. Unfortunately, lockdowns due to the pandemic have not only had a global impact on health, but they have also harmed the economy. Thus, lockdown restrictionswere gradually released with strict obligations on wearing masks and maintaining physical distance. Health professionals from all over the world have since been working tirelessly to obtain drugs and vaccines for the disease [3]. Most COVID-19 infected patients have been diagnosed with pneumonia; thus, radiological examinations can be helpful for diagnosis and evaluation as well as for follow-up on disease progression. Initial screening using chest computed tomography (CT) revealed an over-sensitivity to reverse transcription-polymerase chain reaction (RT-PCR) and also reported COVID-19 infection in negative or low-positive RT-PCR cases. It can sometimes outperform the RT-PCR-based test, which has a low success rate of 70% and a sensitivity of 60-70% [4]. Recent surveys show a pooled sensitivity rate of 94% (95% confidence interval (CI)\(\in(91\%,96\%)\)) but a low specificity rate of 37% (95% CI \(\in(26\%,50\%)\)) for CT-dependent analysis [5]. As a result, CT-dependent analysis could be used to solve the insufficient PCR test sensitivity rate [6]. The most recent COVID-19 research has centred essentially on the observations of chest CTs. Nonetheless, a spike in the increase of COVID-19 prevalence limits the regular use of CT, as it puts a significant burden on patient health due to frequent radiation exposure and possible infections in CT suites. Recent studies claim to obtain reliable results for the automated detection of the disease from chest X-ray (CXR) scans [7]. Since diagnosis has become a relatively rapid operation, the financial problems of diagnostic tests have affected patients worldwide, particularly in countries with private health systems or limited access to health systems due to prohibitive costs. There was an increase in the number of publicly available CXRs from stable cases as well as COVID-19 patients. This helps researchers around the globe analyze medical images and recognise potential trends that may contribute to an automatic diagnosis of the disease [8]. Moreover, CXR tests have the advantages of a fast operating speed, low cost, and ease of use for radiologists [9]. Thus, the need to identify COVID-19 features on CXR is growing [10]. Noninvasive approaches based on artificial intelligence (AI) for the analysis of patient data (e.g., CXRs and CTs) are extensively exploited for the successful diagnosis of COVID-19. In particular, deep learning (DL)-based techniques applied to radiomic features of thoracic imaging, CXR and CT as well as other clinical, pathological and genomics parameters have shown to provide valuable assistance in this direction. In the context of medical image analysis, DL automatically discovers hidden features required for detection and/or disease classification from raw data.