## 2 Related Work
[18] proposed the COVID-19 detection model based on NASNet. The model achieved 97% in both accuracy and sensitivity. [PERSON] et al. [19] proposed a model based on VGG-16 that detects COVID-19 from CXR images. The model achieved a 99% F1 score. They also proposed a model that predicted the cases of COVID-19 confirmations, recoveries, and deaths over the next seven days in Jordan and Australia using three architectures: prophet algorithm (PA), the autoregressive integrated moving average (ARIMA) model, and long short-term memory neural network (LSTM). The empirical results showed that PA achieved an average accuracy of 94.8 and 88.43% in predicting the cases in Australia and Jordan, respectively. To overcome the scarcity of data related to COVID-19 symptoms, [PERSON] et al. [20] used transfer learning techniques and data augmentation to train pre-trained deep CNN models to detect COVID-19 from CXR images. They used CheXNet, a CNN model that consists of 121 layers trained on CXR images to detect different respiratory diseases, in addition to MobileNetv2, SqueezeNet, ResNet18, InceptionV3, ResNet101, VGG19, and DenseNet201. The empirical evaluation showed that all the models achieved high performance in detecting COVID-19, and the highest performance was achieved by both CheXNet and DenseNet201 when the data augmentation technique was applied. [PERSON] et al. [21] introduced two types of hybrid models that use SVM as a classifier in detecting COVID-19 based on CXR images. Each model of the first type consists of a deep pre-trained CNN model, specifically AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201, XceptionNet, MobileNetV2, and ShuffleNet, as the feature extractor, and the extracted features were then fed to SVM to perform the classification task. The second type used traditional image classification methods, e.g., local binary patterns (LBP), a histogram of oriented gradients (HOG), and a gray level co-occurrence matrix (GLCM), with an SVM classifier. The experiment showed that the ResNet50-based hybrid model achieved the best performance with an accuracy of 95.33%. To determine the effect of preprocessing on improving the detection of COVID-19 in CXR images, [PERSON] et al. [22] focused on preprocessing the images using fuzzy and stacking techniques. To evaluate the preprocessing step's effectiveness, two deep learning models, MobileNetV2 and SqueezeNet, with SVM as a classifier, were trained with the original dataset and the preprocessed dataset. The experiment results proved that the preprocessing step improves the classification models. [PERSON] et al. [23] introduced a three-stage architecture that detects and highlights the lung area infected by COVID-19 from CXR images. The model uses VGG-16 in the first and second stages to differentiate between healthy and COVID-19 cases. If the image is classified as COVID-19, the gradient-weighted class activation mapping (Grad-CAM) algorithm is used to generate an activation map that visually illustrates the infected areas. [PERSON] et al. [24] prepared a dataset of 5000 CXR images, called COVID-Xray-5k, that includes 184 CXR images of COVID-19. They compared the performance of four pre-trained convolutional models trained on the COVID-Xray-5k dataset, specifically ResNet18,ResNet50, SqueezeNet, and DenseNet-121. [PERSON] et al. [25] proposed a deep convolutional neural network model, called CoroNet, based on Xception, that is able to distinguish between three types of pneumonia: viral pneumonia, bacterial pneumonia, and COVID-19, from CXR images. [PERSON] et al. [26] comparatively studied different feature extraction approaches with different classification algorithms. To train the models, they prepared a dataset of CXR images called RYDLS-20 with an unbalanced distribution of the classes to reflect the real-world distribution. The dataset has CXR images that belong to normal, majority, and six other pneumonia classes, including COVID-19. [PERSON] et al.