## 3 Chest X-ray Databases  
This study used three databases of chest X-rays freely available to the public (1) the COVID-19 Radiography Database (COVID-19 Radiography Database Kaggle) (Db1) [Available online: [[https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database)]([https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database](https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database)) (accessed on 7 September 2022). ], (2) COVID19 + PNEUMONIA + NORMAL Chest X-Ray Images (COVID19 + PNEUMONIA + NORMAL Chest X-Ray Images | Kaggle) (Db2) [Available online: [[https://www.kaggle.com/datasets/sachinkumar413/covid-pneumonia-normal-chest-xray-images?select=COVID](https://www.kaggle.com/datasets/sachinkumar413/covid-pneumonia-normal-chest-xray-images?select=COVID)]([https://www.kaggle.com/datasets/sachinkumar413/covid-pneumonia-normal-chest-xray-images?select=COVID](https://www.kaggle.com/datasets/sachinkumar413/covid-pneumonia-normal-chest-xray-images?select=COVID)) (accessed on 7 September 2022). ], and (3) Chest X-ray (COVID-19 & Pneumonia) (Chest X-ray (COVID-19 & Pneumonia) | Kaggle) (Db3) [Available online: [[https://www.kaggle.com/datasets/prashant268/chest-xray-covid19-pneumonia](https://www.kaggle.com/datasets/prashant268/chest-xray-covid19-pneumonia)]([https://www.kaggle.com/datasets/prashant268/chest-xray-covid19-pneumonia](https://www.kaggle.com/datasets/prashant268/chest-xray-covid19-pneumonia)) (accessed on 7 September 2022)]. Db1 contains four sets of images (COVID-19, lung opacity, normal, and viral pneumonia) collected by a team composed of researchers from Qatar University and Dhaka University, as well as doctors from Pakistan and Malaysia. It was initially released with 219 COVID-19 images, 1341 normal images, and 1345 viral pneumonia images. It was then updated several times and now includes 3616 COVID-19 images,10,200 normal images, 1345 viral pneumonia images, and 6012 lung opacity images. Figure 1 shows a selection of images from Db1. Db2 contains chest X-ray (CXR) \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Method** & **No. of** & **Classes** & **Accuracy** & **Sensitivity** & **Specificity** \\  & & **Datasets** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{[PERSON], [PERSON] [1]} & AlexNet, & & & AlexNet 99.14 & AlexNet 98.44 & AlexNet 99.9 \\  & GoogleNet, & & & GoogleNet 99.70 & GoogleNet 100 & GoogleNet 99.9 \\  & GoogleNet, & 3 & & SqueezeNet 99.8 & SqueezeNet 100 & SqueezeNet 99.9 \\ \cline{1-1}  & \multirow{4}{*}{CoroNet} & \multirow{4}{*}{3} & AlexNet 96.46 & AlexNet 97.35 & AlexNet 96 \\  & & & & GoogleNet 96.25 & GoogleNet 97.8 & GoogleNet 95.43 \\  & & & & SqueezeNet 96.20 & SqueezeNet 98.1 & SqueezeNet 95.35 \\ \hline [PERSON] et al. [10] & & & Two classes & 99 & 98.6 & - \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{CoroNet} & \multirow{2}{*}{2} & Three classes & 89.6 & 97.5 & - \\ \cline{1-1} \cline{3-6}  & & & & & \\ \cline{1-1} \cline{3-6}  & & & & & \\ \cline{1-1} \cline{3-6}  & & & & & \\ \cline{1-1} \cline{3-6}  & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [13]} & AlexNet & 1 & Two classes & 99.16 & 97.44 & 100 \\ \cline{1-1} \cline{3-6}  & & & & Three classes & 95 & 91.3 & 84.78 \\ \hline [PERSON] et al. [14] & ResNet-101 & 5 & Two classes & 97 & 98 & 98 \\ \hline \multirow{2}{*}{This study} & \multirow{2}{*}{VGG16} & \multirow{2}{*}{3} & Two classes & 99.76 & 100 & 99.68 \\ \cline{1-1} \cline{3-6}  & & & Three classes & 97.5 & 97.58 & 98.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of this study with the related studies. images branched into three subfolders (COVID, NORMAL, PNEUMONIA). It consists of 1626 images of patients with COVID-19, 1802 normal images, and 1800 images of patients with pneumonia. Db3 consists of two main folders (training and testing), and each folder has three sub-folder (COVID-19, pneumonia, and normal).