# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 3 Results
### Results
\begin{table}
\begin{tabular}{l c c c c} \hline
**ALGORITHM USED** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline EfficientNet-B0 & 0.92466 & 0.55814 & 1 & 0.71642 \\ \hline EfficientNet-B3 & 0.96233 & 0.82759 & 1 & 0.90566 \\ \hline EfficientNet-B4 & 0.96233 & 0.82759 & 1 & 0.90566 \\ \hline VGG16 & 0.96575 & 1 & 1 & 1 \\ \hline EfficientNet-B2 & 0.97603 & 0.85714 & 1 & 0.92308 \\ \hline EfficientNet-B5 & 0.97603 & 1 & 1 & 1 \\ \hline EfficientNet-B6 & 0.97603 & 0.88889 & 1 & 0.94118 \\ \hline EfficientNet-B7 & 0.97603 & 0.96 & 1 & 0.97959 \\ \hline Xception & 0.97945 & 1 & 1 & 1 \\ \hline InceptionV3 & 0.98288 & 1 & 1 & 1 \\ \hline EfficientNet-B1 & 0.9863 & 1 & 1 & 1 \\ \hline ResNet11 & 0.9863 & 1 & 1 & 1 \\ \hline DenseNet-201 & 0.9893 & 1 & 1 & 1 \\ \hline ResNet50V1 & 0.98973 & 0.96 & 1 & 0.97959 \\ \hline ResNet50V2 & 0.99315 & 1 & 1 & 1 \\ \hline MobileNetV2 & 0.99658 & 1 & 1 & 1 \\ \hline Majority Voting (hard approach) & 0.99315 & 1 & 1 & 1 \\ \hline Majority Voting (soft approach) & 0.99315 & 1 & 1 & 1 \\ \hline Weighted Voting using Neural & 0.98630 & 1 & 1 & 1 \\ \hline SVM-based voting & 0.98973 & 1 & 1 & 1 \\ \hline Random Forests-based voting & 0.98630 & 1 & 1 & 1 \\ \hline Mean & 0.97945 & 0.94663 & 1 & 0.969104 \\ \hline SD & 0.01602 & 0.10743 & 0 & 0.066476 \\ \hline Confidence Level (95.0\%) & 0.00729 & 0.048903 & 0 & 0.03026 \\ \hline Confidence Interval (95.0\%) & 0.97215- & 0.89773- & -1 & 0.93884-0.99936 \\ \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics of the algorithms on the test dataset. \begin{table}
\begin{tabular}{l c} \hline
**ALGORITHM USED** & **Inference Time (ms)** \\ \hline EfficientNet-B0 & 2 \\ EfficientNet-B3 & 2 \\ EfficientNet-B4 & 1 \\ VGG16 & 0.879 \\ EfficientNet-B2 & 2 \\ EfficientNet-B5 & 2 \\ EfficientNet-B6 & 2 \\ EfficientNet-B7 & 2 \\ Xception & 2 \\ InceptionV3 & 1 \\ EfficientNet-B1 & 2 \\ ResNet11 & 0.996 \\ DenseNet-201 & 2 \\ ResNet50V1 & 2 \\ ResNet50V2 & 1 \\ MobileNetV2 & 2 \\ \hline \end{tabular}
\end{table} 
Table 5: Inference time of the algorithms on the test dataset. As it can be observed in Table 4, the general performance of the models has decreased. This reflects a weak degree of overfitting in most classifiers. Based on the overall accuracy, we can see that some classifiers were more prone to overfitting (like EfficientNet-B7), whereas others had no overfitting (like MobileNetV2 and ResNetV2). In Figure 11, we tried to emphasize more the overall accuracy of the different deep learning models on the test set. We can see that the best classifier is MobileNetV2, which and accuracy of 0.99658. In Figure 12, we show the plots of accuracy and loss of the top 5 best classifiers (MobileNetV2, ResNet50V1, DenseNet-201, ResNet11 and ResNet50V2). The plots are drawn for the training and the validation sets of the Chest X-ray Datasets. These figures demonstrate that all the top models converge efficiently on the training set from the few first epochs. In fact, 100 epochs were sufficient for all the models to converge. However, in the validation set, more epochs were needed to reach the convergence stage, especially for MobileNetV2, ResNet50V1, and ResNet11. Moreover, the overfitting degree was weak in all models (convergence of the Accuracy on the training set is close to its convergence on the validation set). The least prone model to overfitting was ResNet11. Concerning the other metrics, the Precision, the Recall, and the F1 score measured for the class COVID, quasi-optimal were obtained. Beginning by the Precision metric, almost the top classifiers gave a precision of 1 (except ResNet50V1, which gave 0.96). This is illustrated in Figure 13. Hence, this means clinically, that, based on the datasets, the top models have a strong ability to not classify non-COVID cases as COVID cases. This prevents the Medical staff from giving expensive care to individuals that did not suffer from COVID in reality. Concerning the Recall metric, it is more the most important metric to consider for the COVID diagnosis.