Coronavirus 2019 (SARS-CoV-2 or COVID-19) is an infectious disease affecting the respiratory system, which has caused a global pandemic and widespread morbidity and mortality in humans. It has mild to severe symptoms similar to those of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), resulting in multiorgan damage.1 Currently, two tests are available for detection of COVID-19 in the affected patients: diagnostic tests (current infection) and antibody tests (past infection). Diagnostic tests, such as reverse transcription polymerase chain reaction (RT-PCR) and antigen tests, are used for rapid diagnosis of COVID-19. Since false positives (FPs) are more common in antigen tests, RT-PCR is used as the gold standard for diagnosis of the disease. RT-PCR tests require an intensive lab work to acquire the results,2 and the cost of the test is a major concern in many countries that have a private health system. Although the PCR and antigen test can now provide a rapid diagnosis, the assessment of the lungs using medical imaging will provide information on disease burden. Also, faster and earlier detection of COVID-19 would help in isolating the affected patients sooner to alleviate the disease spread. Chest radiography (CXR) and computed tomography (CT) images are the conventional medical imaging modalities used in lung disease diagnosis.3,4 Though CT images are extensively used in the COVID-19 diagnosis,5–7 cost8 and radiation exposure are major concerns. CXR are preferred over CT images as they have less exposure to radiation and extensively available.9 Hence, in this study, CXR images are used for automatic diagnosis of COVID-19. Deep learning techniques are widely used in various fields, such as computer vision, machine vision, and speech recognition, among which computer vision is one of the most popular fields in which promising results to have been obtained in image classification tasks.10–12 In medical image analysis, deep learning has been widely investigated for computer-aided diagnosis and treatment.13,14 Several state-of-art methods have been proposed for the diagnosis of COVID-19 using CXR images15–19 and CT images5,20,21 based on deep learning techniques. The transfer learning approaches based on deep learning have been preferred in detection of COVID-19 due to the limited available dataset. Several studies have been implemented for COVID-19 diagnosis using the pretrained model as a feature extractor by implementing transfer learning techniques.22–24 Table 1 summarizes recent studies describing methods developed for detecting COVID-19 from CXR images; the overall datasets used for training and testing the model, accuracy, and the sensitivity are provided. Ozturk et al.18 developed the DarkCovidNet model for the detection of COVID-19 using multiclass classification. The model was developed using end-to-end architecture without adding any feature extraction techniques. Sensitivity of 85.35% was achieved by the proposed model. Wang and Wong25 proposed COVID-Net, a deep convolutional neural network (DCNN) developed by adopting machine-driven design exploration strategy. A multiclass classification was performed using CXR images in identifying COVID-19 from normal and non-COVID disease (i.e., pneumonia). Both qualitative and quantitative analysis were performed to determine the success of the model. The developed model was able to achieve a sensitivity of 91% for detecting COVID-19. Panwar et al.26 introduced nCOVnet, an algorithm developed based on a transfer learning model for faster diagnosis of COVID-19 from CXR images. The proposed model is built using VGG 16 as the base model. The model was able to achieve 97.62% in detecting COVID-19. Sethy et al.23 described a comparative study on Xception, ResNet18, ResNet50, Resnet 101, Inceptionv3, Inceptionresnetv2, GoogleNet, Densenet201, VGG16, VGG19, and AlexNet using the deep features extracted from the CNN layers and fed to the SVM classifier for classification. ResNet50 performed better compared with other classification models on a binary classification between viral pneumonia and COVID-19 with a classification accuracy of 95.38%. Jain et al.27 utilized ResNet50 model that was used in stage-I network model for distinguishing viral (including COVID-19) from bacterial pneumonia and normal cases using CXR images. Based on the result obtained, ResNet101 was used in stage-II network model to classify COVID-19 from other viral pneumonia. The model achieved an accuracy of 97% in COVID-19 detection. Narin et al.28 proposed three CNNs for the detection of COVID-19 through CXR images. The pretrained models chosen for comparative study were ResNet50, InceptionV3, and Inception-ResNetV2. A binary classification was performed between normal and COVID-19. ResNet50 model outperformed all the other models with the highest classification accuracy of 98%. Ioannis et al.22 compared VGG 19, Mobile Net, Inception, Xception, and Inception ResNetv2 pretrained models for the automatic detection of COVID-19 using CXR images. They implemented multiclass classification among COVID-19, normal, and pneumonia. MobileNetV2 achieved highest sensitivity rate of 98.6%. Asnaoui et al.29 put forward a comparative study on the pretrained DCNN models, namely VGG16, VGG19, Inception-ResNetV2, InceptionV3, ResNet50, DenseNet201, and MobileNetV2 for the classification of CXR into normal, bacteria, and coronavirus (multiclass classification). Inception-ResNetV2 model provided a sensitivity rate of 92.11% in detecting coronavirus. Hemdan et al.30 proposed COVIDX-Net a deep learning framework based on seven DCNNs namely, VGG19, Xception, ResNetV2, InceptionV3, Inception-ResNetV2, DenseNet201, and MobileNetV2 for the diagnosis of COVID-19 using x-ray images. The VGG19 and DenseNet201 achieved an accuracy of 90% compared with other models with F1-score of 0.89 for normal and 0.91 for COVID-19. Table 1 Methods described for diagnosis of COVID-19 from CXR and CT images.