## 2 Related Work
Specifically, these studies applied traditional GANs or other GAN architectures, including CGAN, auxiliary classifier GAN (ACGAN), and conditional variational auto-encoder GAN (CVAE-GAN). In the following, we provide a summary of the related studies. [PERSON] et al. [9] developed a simple convolutional neural network (CNN) and a modified AlexNet model to classify CXR and CT scan images. The results of the study showed that the two applied models provided accuracies of 94.1% and 98%, respectively. [PERSON] and [PERSON] [10] introduced a customized convolutional neural network, i.e., COVID-Net, for COVID-19 cases diagnosis and made the used dataset available for public use. The model achieved an accuracy of 92.4%. [PERSON] et al. [11] proposed three different convolutional network models to detect COVID-19 patients, namely, ResNet50, InceptionV3, and Inception-ResNetV2. The results showed that the ResNet50 model obtained the highest recognition performance, with 98% accuracy, compared to the other two proposed models. [PERSON] et al. [12] used 100 CXR images of COVID-19 patients and another 1431 CXR images of other pneumonia cases. Based on this dataset, the authors developed a new deep learning model for detection, which achieved a sensitivity of 96%. [PERSON] et al. [13] used pre-trained Resnet50 and VGG16 plus their own small CNN trained on 102 COVID-19 cases and 102 other pneumonia cases in 10-fold cross-validation. Their model achieved an accuracy of 94.4%. [PERSON] and [PERSON] [14] proposed a public dataset for COVID-19 cases and other pneumonia cases to be used by the public. The authors also proposed a novel model, i.e., COVID-ResNet, that was tested to reduce training time. The model achieved an accuracy of 96.23%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Image Type**} & \multicolumn{2}{c}{**Dataset Size**} & \multirow{2}{*}{**GAN Used**} & \multirow{2}{*}{**Classification Method**} & **Model** \\  & & **(of COVID-19 Cases)** & & & **Performance** \\ \hline
[6] & \multirow{2}{*}{CXR} & \multirow{2}{*}{403} & \multirow{2}{*}{ACGAN} & \multirow{2}{*}{VGG16} & 95\% (Acc.) \\  & & & & & 96 \% (Prec.) \\ \hline
[7] & \multirow{2}{*}{CXR} & \multirow{2}{*}{69} & Traditional & \multirow{2}{*}{AlexNet, GoogleNet, Resnet18} & \multirow{2}{*}{100\% (Acc.)} \\  & & & & & \\ \hline
[8] & \multirow{2}{*}{CXR} & \multirow{2}{*}{337} & Customized & \multirow{2}{*}{InceptionV3, ResNet} & \multirow{2}{*}{85–87\% (Acc.)} \\  & & & & & \\ \cline{5-5}  & & & & & \\ \hline
[32] & \multirow{2}{*}{CT} & \multirow{2}{*}{345} & \multirow{2}{*}{CGAN} & AlexNet, VGGNet16, VGGNet19, & \multirow{2}{*}{81.41\% (Acc.)} \\  & & & & & \\ \cline{5-5}  & & & & & \\ \hline \hline \end{tabular}  
* Acc.: Accuracy; Sens.: Sensitivity; Prec.: Precision; Spec.: Specificity; AUC: Area under the curve.  
\end{table} 
Table 1: _Cont._[PERSON] et al. [16] proposed a number of deep learning models, i.e., ResNet34, ResNet50, DenseNet169, VGG-19, Inception ResNetV2, and RNN, to detect pneumonia infection cases, notably viral cases. They claimed that cases of viral pneumonia diagnosed during the COVID-19 period are extremely likely to be COVID-19 infections. The DenseNet169 architecture reached the best performance with an average classification accuracy of 95.72% on the CXR Images Pneumonia dataset. [PERSON] et al. [17] proposed a CNN-based hybrid model for detecting COVID-19. The models used CNN as a feature extractor, and the extracted features were applied as the input to classical machine learning techniques, specifically k-nearest neighbor, support vector machine (SVM), and decision tree, to perform the classification task. The CNN-SVM model achieved 98.97% accuracy, 89.39% sensitivity, 99.75% specificity, and 96.72% F1-score. Neural architecture search network (NASNet) is a deep convolutional network architecture developed by the Google Brain team in 2018. NASNet effectively shows improved performance on classification tasks. [PERSON] et al.