# 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 & 97.36\% & 97.86\% & 97.70\% & 99.63\% & 95.80\% \\ InceptionV3 & 93.23\% & 95.86\% & 94.70\% & 99.60\% & 86.40\% \\ VGG-16 & 95.93\% & 95.23\% & 96.37\% & 99.03\% & 92.43\% \\ VGG-19 & 95.97\% & 96.13\% & 96.63\% & 99.30\% & 93.30\% \\ ResNet50 & 93.63\% & 94.30\% & 94.67\% & 99.00\% & 90.90\% \\ ResNet152 & 93.60\% & 90.37\% & 91.70\% & 94.90\% & 89.50\% \\ MobileNetV2 & 97.23\% & 98.33\% & 95.43\% & 99.67\% & 94.63\% \\ DenseNet201 & 95.80\% & 96.43\% & 97.07\% & 99.70\% & 93.73\% \\ InceptionResNetV2 & 92.23\% & 92.40\% & 93.83\% & 96.47\% & 83.73\% \\ EfficientNetB7 & 93.03\% & 92.93\% & 94.83\% & 99.67\% & 88.50\% \\ NasNetMobile & 96.67\% & 97.40\% & 97.43\% & 99.77\% & 94.63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification results of normal, pneumonia and COVID-19 using the cropped lungs and 11 CNNs with Hybrid sampling (Approach 5). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Classification Methods** & **Classification Strategy** & **Data** & **Performance** \\ \hline \multirow{2}{*}{[PERSON] et al. [10]} & COVIDX-Net using six CNNs & Binary classification of normal and COVID-19 & COVID-19: 25 & F-Measure \\  & COVIDX-Net using six CNNs & Binary classification of healthy & COVID-19: 25 & 81\% (normal) \\  & COVIDX-Net using six CNNs & Binary classification & 91\% (COVID-19) \\ \hline \multirow{3}{*}{[PERSON] et al. [11]} & Hybrid of InceptionNet as a feature extractor and Marine & Binary classification between COVID-19 & COVID-19: 200 \\  & Predator as a feature & Binary classification & Normal: 1675 \\  & Predator as a feature & between COVID-19 & COVID-19: 219 \\  & selection method & and Normal & Normal: 1341 \\ \hline \multirow{3}{*}{[PERSON] et al. [12]} & VGG-16 based classification & \multirow{3}{*}{Binary classification of healthy and COVID-19} & COVID-19: 70 \\  & VGG-16 based classification & & Healthy: 28 \\  & with and without & & (original dataset) \\  & data augmentation & and COVID-19 & COVID-19: 500 \\  & & & Healthy: 500 \\  & & & (augmented) \\ \hline \multirow{2}{*}{[PERSON] et al. [13]} & A customized deep learning model & Binary classification of non-findings and COVID-19 & COVID-19: 2589 \\  & learning model & non-findings and COVID-19 & Normal: 4337 \\ \hline \multirow{2}{*}{[PERSON] et al. [14]} & 2D CNN, VGG16, Mobile Nets & Binary classification of normal and COVID-19: 1210 \\  & & normal and COVID-19 & Normal: 1583 \\ \hline \multirow{2}{*}{[PERSON] et al. [15]} & A custom deep learning algorithm & Multi-classification of normal, COVID-19 and pneumonia & COVID-19: 266 \\  & learning algorithm & normal, COVID-19 and pneumonia & Normal: 8066 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: False-negative of COVID-19 (misclassification of COVID-19 CXRs as normal and pneumonia). Figure 17: The indicative regions of COVID-19 biomarkers highlighted by DenseNet201. Here, the empirical results indicate strong evidence that of the evaluated approaches, coupling in the ensemble learning can suppress or alleviate the unbalanced class distribu