# COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning
## 1 Introduction  
### Background  
The infectious coronavirus disease-2019 (COVID-19) or SARS-CoV-2 virus rapidly spread and was declared a pandemic by the World Health Organization (WHO) in March 2020 [1]. As of early October 2022, the recorded number of confirmed cases worldwide was 624,142,119, and the death toll was 6,552,954 [2]. COVID-19 is a viral disease that causes severe fever, a cough, shortness of breath, sore throats, headaches, diarrhea, a loss of taste, fatigue, and a loss of smell. Severe respiratory failure can lead to death [3]. Accordingly, screening for COVID-19 confirmed cases and responding to quarantine measures are essential. The early identification of confirmed cases through a polymerase chain reaction (PCR) test and X-ray is required for screening COVID-19 confirmed cases and implementing a suitable response (quarantine, treatment, and recovery). However, it has been pointed out that the PCR test is expensive, and it takes considerable time to confirm the test results [4]. The budgeting of the expenditure resulting from the high cost of testing and the input of a professional workforce for each testing location is critical. Therefore, the economic burden of government spending is high. It was determined that COVID-19 is a respiratory lung disease that is also one of the mutations of pneumonia. Diagnosis using a chest X-ray (CXR) for the early detection of lung disease is effective because it requires less time and money [5]. Compared to CT, X-ray imaging is cheaper, faster, and more readily accessible, and it exposes the body to far lesshazardous radiation [6]. Therefore, X-ray images are an effective instrument for rapidly diagnosing pneumonia and COVID-19 in patients [7]. According to the WHO recommendation regarding chest imaging for COVID-19, chest X-ray images are recommended as complementary information for those who test negative in the initial PCR test but have suspicious COVID-19 symptoms [8]. As a result of an X-ray and CT of the patient with a false negative PCR test result, 70% of COVID-19 symptoms were confirmed by chest radiography or a chest CT [9; 10]. Using X-ray images can help prevent and manage the prognosis of diseases by visualizing infected lungs along with a confirmation of lung infection [11; 12]. In addition, this information can help professionals take timely decisions on admission and discharge criteria in terms of the infection stage, level of care, and patient care modalities and help to secure the need for ICU beds [3; 9; 13; 14]. Accordingly, X-ray imaging, as an auxiliary tool, can help diagnose pneumonia and COVID-19 in patients [8; 11; 15]. Therefore, by using an AI model to accurately classify X-rays and enable the early screening of confirmed cases, it is possible not only to suppress the spread of disease in the community but also to reduce the cost of testing. It can also help to ensure a pre-emptive quarantine response during a pandemic that causes chaos [16; 17]. Owing to the inherent qualities of machine vision, artificial intelligence (AI) and machine learning technology have achieved outstanding outcomes in the medical imaging area [18]. In an era in which COVID-19 is spreading across the world, machine learning researchers and computer scientists play a crucial role. Deep learning is one of the breakthroughs in AI. It collects the fine-grained features from the images [19]. It is a set of machine learning techniques primarily focused on automatically extracting and classifying features from images. Many challenges, including arrhythmia detection, skin cancer classification, breast cancer detection, brain disease classification, and pneumonia identification from chest X-ray images, have been successfully addressed with deep learning techniques [20]. Convolutional neural networks (CNNs) have shown promise in the analysis and classification of X-ray images using deep learning techniques [3].