# Detecting COVID-19 Effectively with Transformers and CNN-Based Deep Learning Mechanisms
## 1 Introduction  
COVID-19 is a contagious respiratory disease caused by the SARS-CoV-2 virus. It can cause pneumonia, which is an infection that inflames the air sacs in the lungs. COVID-19 pneumonia can be severe and life-threatening, particularly for older individuals or those with underlying health conditions. Symptoms of COVID-19 pneumonia include cough, fever, shortness of breath, chest pain, and fatigue. Early detection of COVID-19 is crucial, as the virus can progress rapidly, leading to the need for intensive care and respiratory support. Medical professionals have difficulty distinguishing COVID-19 pneumonia from other types of pneumonia based on physical symptoms alone, so accurate testing methods are essential for prompt diagnosis and treatment [1]. Following the outbreak of COVID-19, the world has faced significant challenges, and insufficient testing has been identified as one of the underlying causes of the rapid spread of the virus. One of the significant problems that medical professionals faced was the time it took to obtain results from the nucleic acid amplification tests (NAATs) and antigen tests. Although these tests are effective, with some versions, such as RT-PCR and RDT, they have proven insufficient due to the time it takes to get results and insufficient quantities of available tests [2]. To address this issue, researchers began exploring alternative testing methods to boost the detection of COVID-19. Computerized tomography (CT) scans are one of the alternative methods that have been utilized to aid in the rapid detection of COVID-19. Medical professionals have used CT scans as a complementary tool for COVID-19 diagnosis to detect lung damage or other abnormalities that may suggest the presence of the virus. The use of CT scans for COVID-19 detection is a non-invasive and rapid method that can provide results in a matter of minutes. Recent studies have shown that the severity of lung abnormalities quantified on chest CT can be used in the detection of COVID-19 and the evaluation of disease severity and progression. While concerns have been raised about the number of false positives and negatives associated with CT scans, it has become a preferred method of preliminary investigation for some medical facilities to detect COVID-19 even at early stages [3; 4; 5]. [PERSON] et al. [6] concluded that the 25-point CT severity score correlates well with the clinical severity of COVID-19, indicating that the chest CT scoring system can aid in predicting the presence of the COVID-19 disease. [PERSON] et al. [7] and [PERSON] et al. [8] proved that the patterns of CT findings, that is, the severity of lung abnormalities quantified on chest CT, can be used in the detection of COVID-19 and evaluation of the disease severity and progressive stages. Certain CT characteristics such as consolidation, GGO (ground-glass opacity), and crazy-paving signs have been identified as indicators for detecting and evaluating COVID-19 [9]. In general, although conventional viral testing approaches continue to be considered the most reliable method for diagnosing COVID-19, the use of CT scans has emerged as an important tool for the prompt detection and evaluation of the disease. However, interpreting these scans necessitates a high level of expertise in radiology, and there are limited numbers of professionals who possess this skill, which may lead to errors in the interpretation of results [4]. As a result, researchers have started exploring alternative, quicker, and more straightforward approaches for identifying COVID-19. One such approach involves utilizing machine learning techniques on both X-ray images and CT scans. Machine learning, which is a subset of artificial intelligence, automates the development of analytical models by examining data [10; 11; 12]. The subdivision of machine learning that creates models specifically for identifying COVID-19 through CT scans is commonly referred to as computer vision. In computer vision, one of the techniques that are frequently used is deep learning.