# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
## 4 Experimental Setting, Results and Discussions
### Experimental Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deep CNNs** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** & **F-Measure** \\ \hline XceptionNet & 97.40\% & 99.37\% & 94.97\% & 99.90\% & 95.17\% \\ InceptionV3 & 86.67\% & 87.20\% & 80.23\% & 99.03\% & 77.83\% \\ VGG-16 & 83.90\% & 99.33\% & 74.07\% & 98.60\% & 72.77\% \\ VGG-19 & 86.03\% & 94.83\% & 69.60\% & 96.73\% & 78.30\% \\ ResNet50 & 86.63\% & 98.83\% & 76.53\% & 98.87\% & 83.57\% \\ ResNet152 & 94.27\% & 98.27\% & 89.50\% & 99.33\% & 88.20\% \\ MobileNetV2 & 97.23\% & 98.33\% & 95.43\% & 99.67\% & 94.63\% \\ DenseNet201 & 95.47\% & 99.40\% & 89.63\% & 99.67\% & 93.63\% \\ InceptionResNetV2 & 97.17\% & 99.07\% & 94.00\% & 99.80\% & 95.30\% \\ EfficientNetB7 & 83.37\% & 94.87\% & 64.83\% & 97.47\% & 80.43\% \\ NasNetMobile & 93.13\% & 94.07\% & 94.20\% & 99.17\% & 87.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results of normal, pneumonia, and COVID-19 using the cropped lungs and 11 CNNs (Approach 0: Unbalanced dataset). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deep CNNs** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** & **F-Measure** \\ \hline XceptionNet & 97.07\% & 97.13\% & 92.77\% & 99.80\% & 96.20\% \\ InceptionV3 & 95.10\% & 98.57\% & 93.63\% & 99.57\% & 91.60\% \\ VGG-16 & 83.97\% & 99.87\% & 67.40\% & 99.30\% & 74.77\% \\ VGG-19 & 87.60\% & 97.27\% & 79.87\% & 97.80\% & 82.80\% \\ ResNet50 & 97.70\% & 99.47\% & 96.37\% & 99.90\% & 95.23\% \\ ResNet152 & 95.83\% & 97.27\% & 91.53\% & 99.53\% & 93.40\% \\ MobileNetV2 & 97.17\% & 98.07\% & 94.57\% & 98.93\% & 94.13\% \\ DenseNet201 & 95.97\% & 95.43\% & 96.67\% & 98.37\% & 93.23\% \\ InceptionResNetV2 & 96.53\% & 98.67\% & 94.53\% & 99.70\% & 93.97\% \\ EfficientNetB7 & 88.43\% & 89.37\% & 91.50\% & 98.23\% & 83.80\% \\ NasNetMobile & 97.97\% & 98.97\% & 96.03\% & 99.90\% & 94.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results of normal, pneumonia, and COVID-19 on the whole CXRs using 11 CNNs (Approach 0 without lung segmentation: Unbalanced dataset). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deep CNNs** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** & **F-Measure** \\ \hline XceptionNet & 98.17\% & 98.33\% & 98.53\% & 99.93\% & 97.07\% \\ InceptionV3 & 93.50\% & 95.13\% & 95.23\% & 99.67\% & 88.47\% \\ VGG-16 & 96.73\% & 95.33\% & 97.10\% & 99.33\% & 94.03\% \\ VGG-19 & 91.60\% & 85.50\% & 88.70\% & 98.07\% & 83.90\% \\ ResNet50 & 97.63\% & 96.93\% & 97.90\% & 99.70\% & 95.10\% \\ ResNet152 & 97.40\% & 96.03\% & 97.53\% & 99.47\% & 95.33\% \\ MobileNetV2 & 97.57\% & 97.50\% & 97.53\% & 99.63\% & 95.20\% \\ DenseNet201 & 98.00\% & 97.90\% & 98.50\% & 99.80\% & 95.67\% \\ InceptionResNetV2 & 96.63\% & 95.53\% & 96.10\% & 99.30\% & 94.17\% \\ EfficientNetB7 & 97.13\% & 98.10\% & 97.70\% & 99.87\% & 92.10\% \\ NasNetMobile & 95.93\% & 97.13\% & 92.47\% & 99.77\% & 93.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results of normal, pneumonia and COVID-19 using cropped lungs and 11 CNNs with Weighted Loss (Approach 1). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deep CNNs** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** & **F-Measure** \\ \hline XceptionNet & 97.60\% & 98.23\% & 98.07\% & 99.90\% & 95.43\% \\ InceptionV3 & 95.80\% & 97.00\% & 96.83\% & 99.83\% & 92.33\% \\ VGG-16 & 95.17\% & 95.90\% & 96.53\% & 99.50\% & 92.23\% \\ VGG-19 & 95.67\% & 94.47\% & 94.03\% & 99.07\% & 92.03\% \\ ResNet50 & 97.13\% & 97.40\% & 97.57\% & 99.70\% & 94.97\% \\ ResNet152 & 96.43\% & 97.03\% & 95.77\% & 99.27\% & 93.50\% \\ MobileNetV2 & 98.43\% & 98.20\% & 98.23\% & 99.83\% & 96.20\% \\ DenseNet201 & 98.57\% & 98.17\% & 97.40\% & 99.87\% & 97.70\% \\ InceptionResNetV2 & 98.50\% & 98.50\% & 98.50\% & 98.87\% & 97.43\% \\ EfficientNetB7 & 93.87\% & 95.40\% & 95.50\% & 98.67\% & 85.50\% \\ NasNetMobile & 98.20\% & 98.33\% & 98.47\% & 99.87\% & 96.73\% \\ \hline \hline \end{tabular}
\end{table}