## 1 Introduction  
The coronavirus (COVID-19) outbreak has resulted in a worldwide crisis; globally, the rate of cases has risen rapidly. The World Health Organization (WHO) has reported 131,020,967 confirmed COVID-19 cases and 2,850,521 deaths worldwide as of 6 April 2021 [1]. To investigate COVID-19 symptoms, a chest X-ray (CXR) imaging is considered a first-line diagnostic examination. However, medical providers may suffer from a lack of sufficient experience to conduct accurate diagnoses based on medical images such as CXR and computed tomography (CT) due to the rapid and vast changes in the COVID-19 virus. Therefore, artificial intelligence (AI), especially deep learning techniques, can be applied as powerful tools to support practitioners' efforts to automate diagnostic procedures for COVID-19. Deep learning solutions show promise as tools for developing medical-image-based solutions for diagnosing COVID-19. Diagnosis via medical images using deep learning techniques can provide precise and concise analysis results.  
One of the main concerns is the lack of available medical image datasets related to COVID-19, such as CXR and CT scans; the available datasets are considered small and insufficient in terms of their size. This was expected, as COVID-19 is new to research, which has resulted in the collection of small, relevant, labeled datasets and a lack of standardized data. This is a major challenge for diagnosing COVID-19 using deep learning models, where the size of medical image samples is insufficient for meaningful learning in the clinical practice context [2].  
Although existing deep learning detection models of COVID-19 achieve high performance, these models may not be trusted as they are based on a small dataset. Overfitting is an analysis that very closely resembles a specific dataset, and therefore most fail to properly fit new unseen observations. Overfitting is a common problem that occurs with deep learning models that are trained on limited datasets.  
To overcome this issue, we suggest the use of generative adversarial networks (GANs) [3]. GANs can generate new images by learning the data distribution of the original dataset. In this study, a conditional GAN (CGAN), which is a special type of the basic GAN, was designed specifically for generating COVID-19 CXR images. CGAN [4], which extends the GAN, can generate specific class data samples, for example, by manipulating an image label that is a part of dataset metadata. CGAN was chosen over the basic GAN as the quality of the sample generation process is enhanced due to the direct CGAN training process [5; 6]. In particular, we propose COVID-CGAN, a detection model for COVID-19 patients using multiple deep learning models based on the CXR images generated by the designed CGAN. Several researchers [6; 7; 8] have applied GANs to synthesize COVID-19 images based on CXR or CT images. However, these studies did not create detection models to diagnose COVID-19 based on the generated images. Some other researchers constructed deep learning detection models for COVID-19, but such studies were mainly based on relatively small datasets [9; 10; 11; 12; 13; 14; 15; 16].  
The main contributions of this paper are as follows:  
* A customized CGAN was designed to generate CXR images that can be used for COVID-19 detection studies. This includes the architectures of generator and discriminator networks as well as parameter configurations;
* An expanded COVID-19 CXR dataset that involves 3290 images that can be used to build COVID-19 detection models was generated;
* COVID-19 detection using the synthetic CXR images generated by the CGAN was demonstrated.  
This paper is structured as follows: Section 2 presents the related work. Section 3 describes the materials and methods used in our experimental work. Section 4 presents the results, and Section 5 discusses the main findings and concludes the paper.