###### Abstract  
Coronavirus disease (COVID-19) is a viral pneumonia that originated in China and has rapidly spread around the world. Early diagnosis is important to provide effective and timely treatment. Thus, many studies have attempted to solve the COVID-19 classification problems of workload classification, disease detection, and differentiation from other types of pneumonia and healthy lungs using different radiological imaging modalities. To date, several researchers have investigated the problem of using deep learning methods to detect COVID-19, but there are still unsolved challenges in this field, which this review aims to identify. The existing research on the COVID-19 classification problem suffers from limitations due to the use of the binary or flat multiclass classification, and building classifiers based on only a few classes. Moreover, most prior studies have focused on a single feature modality and evaluated their systems using a small public dataset. These studies also show a reliance on diagnostic processes based on CT as the main imaging modality, ignoring chest X-rays, as explained below. Accordingly, the aim of this review is to examine existing methods and frameworks in the literature that have been used to detect and classify COVID-19, as well as to identify research gaps and highlight the limitations from a critical perspective. The paper concludes with a list of recommendations, which are expected to assist future researchers in improving the diagnostic process for COVID-19 in particular. This should help to develop effective radiological diagnostic data for clinical applications and to open future directions in this area in general. artificial intelligence; COVID-19; CXR; CT-scan; deep learning; diagnosis; image classification; multi-classes; pneumonia +
Footnote †: journal: Medical Imaging Techniques: A Literature Review  
many types of viruses, including SARS-CoV-2. The technique involves taking a sample from a part of the body, such as the nose, to extract the virus' RNA [4]. Physicians are facing difficulties due to the limitations of this method, which include long waiting times for results, low availability of examination kits, and suboptimal sensitivity [5]. These limitations can compromise patients and further the spread of COVID-19. In urgent cases, physicians can ask radiologists to complete the diagnostic process using radiological imaging modalities such as chest X-rays (CXRs) or computed tomography (CT) scans of the lungs. These images are then analyzed by a specialist to reach a final diagnosis. Accurate analysis of these medical images can help to overcome the limitations of RT-PCR [6]. However, the limited time available for radiologists to screen medical images and differentiate between COVID-19 infection and other lung diseases--especially under time constraints and high patient numbers--makes completing these processes difficult. In addition, radiologists with different skills and lower diagnostic accuracy may lead to an inaccurate diagnosis, producing incorrect results [7]. A recent trend in healthcare involves deploying artificial intelligence (AI) algorithms in image classification problems (e.g., identifying cardiovascular abnormalities, detecting fractures and other musculoskeletal injuries, and diagnosing neurological diseases) [8]. High-accuracy AI models have been developed through training on medical images, yielding promising results after learning complex problems in radiology. More broadly, machine learning and deep learning models have produced effective results in several key aspects of radiology [9]. Given the extent of the recent pandemic, many of the studies that are reviewed in Section 4 have used deep learning techniques to diagnose and detect COVID-19 pneumonia using medical imaging in a theoretical way that cannot be deployed clinically. Accordingly, there are many review articles in this field that highlight the efficiency of deep learning algorithms and imaging modalities for the diagnosis of COVID-19 [10; 11].