## 2 Related Work  
This section provides an overview of the state-of-the-art studies in relation to COVID-19 detection based on CXR images. The section also provides a summary of previous studies suggesting that applied GANs are able to overcome the problem posed by the small COVID-19 dataset size. Table 1 presents a summary of COVID-19-related studies in terms of image type used, dataset size, GAN architecture applied to expand the dataset in cases where GANs are used, classification method, and classification model performance. \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**} & \multicolumn{1}{c}{**Model**} \\  & & **(of COVID-19 Cases)** & & & **Performance** \\ \hline \hline
[9] & CXR & 170 & - & AlexNet & 98\% (Acc.) \\ \hline
[10] & CXR & 183 & - & Customized CNN model (COVID-Net) & 92.6\% (Acc.) \\ \hline
[11] & CXR & 100 & - & ResNet50, InceptionV3, Inception-ResNetV2 & 98\% (Acc.) \\ \hline
[12] & CXR & 100 & - & Customized CNN model & 96\% (Acc.) \\ \hline
[13] & CXR & 102 & - & ResNet50 and VGG16 & 94.4\% (Acc.) \\ \hline
[14] & CXR & 68 & - & ResNet-50 & 96.23\% (Acc.) \\ \hline
[16] & CXR & 145 & - & ResNet34, ResNet50, DenseNet169, VGG19, Inception ResNetV2, RNN & 95.72\% (Acc.) \\ \hline
[17] & CXR & 219 & - & KNN, SVM, DT & 98.97\% (Acc.) \\  & & & & 89.39\% (Sens.) \\  & & & & 99.75\% (Spec.) \\  & & & & 96.72\% (F1-score) \\ \hline
[18] & CXR & 120 & - & NASNet & 97\% (Acc.) \\ \hline
[19] & CXR & 70 & - & VGG16 & 99\% (F1-score) \\ \hline
[20] & CXR & 423 & - & MobileNet2, SqueezeNet, ResNet18, InceptionV3, ResNet101, CheXNet, VGG19, DenseNet201 & 99.7\% (Sens.) \\ \hline
[21] & CXR & 127 & - & SVM & 95.33\% (Acc.) \\ \hline
[22] & CXR & 295 & - & SVM & 99.27\% (Acc.) \\ \hline
[23] & CXR & 250 & - & VGG16 & 0.98 (F-1 score) \\  & & & & 0.94 (Spec.) \\  & & & & 0.87 (Sens.) \\ \hline
[24] & CXR & 184 & - & ResNet18, ResNet50, SqueezeNet, DenseNet-121 & 98\% (Sens.) \\ \hline
[25] & CXR & 284 & - & Xception & 93\% (Prec.) \\  & & & & 98.2\% (Sens.) \\ \hline
[26] & CXR & 90 & - & KNN, SVM, MLP, DT, RF & 0.89 (F-1 score) \\ \hline
[27] & CXR & 127 & - & DarkNet & 98.08\% (Acc.) \\ \hline
[28] & CXR & 132 & - & VGG16 & 86\% (Sens.) \\  & & & & 93\% (Spec.) \\  & & & & 90\% (AUC) \\ \hline
[29] & CXR & 180 & - & Patch-based CNNs that consist of a number of ResNets & 88.9\% (Acc.) \\  & & & & 84.4\% (Sens.) \\  & & & & 96.4\% (Spec.) \\ \hline
[30] & CXR & 25 & - & NB, NN, SVM, RBF, KNN, SGD, LR, RE, DT, AdaBoost & A table of 12 classifiers and 10 evaluation criteria \\ \hline
[31] & CXR & 313 & - & Customized CNN, VGG16, VGG19, Inception-V3, Xception, InceptionResNet-V2, MobileNet-V2, DenseNet-201, NasNet-mobile & 99.01\% (Acc.) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of COVID-19-related studies. From Table 1, we can draw the following observations:  
* The majority of the studies used small datasets to feed their classification models. The maximum size of these datasets was 423 images. Some of these studies used internal datasets that have not been published for public use;
* Although most of the models achieved high accuracies, the prediction results based on them cannot be generalized due to the limited samples on which the models were trained;
* There are no studies that applied CGANs to CXR. To the best of our knowledge, one study applied a CGAN to CT but did not achieve more than 81.41% accuracy. The studies shown in Table 1 applied several machine learning methods such as support vector machines (SVM) and random forest (RF) as well as deep learning methods such as AlexNet, GoogleNet, SqueezeNet, and ResNet18. Most of these methods achieved high performance in detecting COVID-19 cases. Moreover, only a few studies [6; 7; 8; 17] applied GANs to expand the COVID-19 datasets.