# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 4 Conclusions  
In this study, we targeted the COVID diagnosis task from the chest X-ray images. We began by preparing the dataset to be used. We selected the deep learning models to best tested among the current state of the art algorithms in image classification. We modified their architecture to add our designed head model. We performed data augmentation and made the training of all the selected classifiers on the processed dataset. We found  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Test Set** & **Validation Set** \\ \hline Majority Voting (hard approach) & 95\% & 100\% \\ \hline Majority Voting (soft approach) & 95\% & 100\% \\ \hline ResNet11 & 86\% & 100\% \\ \hline DenseNet-201 & 90\% & 94\% \\ \hline MobileNetV2 & 100\% & 84\% \\ \hline EfficientNet-B7 & 71\% & 100\% \\ \hline ResNet50V2 & 95\% & 84\% \\ \hline Xception & 76\% & 88\% \\ \hline ResNet50V1 & 90\% & 75\% \\ \hline InceptionV3 & 81\% & 75\% \\ \hline EfficientNet-B1 & 86\% & 69\% \\ \hline EfficientNet-B5 & 71\% & 75\% \\ \hline VGG16 & 57\% & 78\% \\ \hline EfficientNet-B2 & 71\% & 62\% \\ \hline EfficientNet-B3 & 52\% & 62\% \\ \hline EfficientNet-B6 & 71\% & 47\% \\ \hline EfficientNet-B4 & 52\% & 56\% \\ \hline EfficientNet-B0 & 0\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Accuracy improvement of the selected methods proportionally to the margin between the best and the least recorded accuracy on the test and the validation sets.  
very encouraging results when testing on the test set. All the classifiers got an accuracy of around 98%. The recall was 100% for all of them, which has an important clinical advantage. This means the labeling of COVID cases to other classes is almost zero, which reduces the risk of non-detecting COVID cases from their chest X-rays. To go beyond in improving the accuracy, we selected the top-performing classifiers on the test set and designed five different Ensemble Voting methods. To reinforce our findings, we made the experiments on two different sets (the test set and the validation set). We found that the best approach to be adopted for COVID diagnosis is the Majority Voting method based on the results given by the top selected classifiers on the test set: MobileNetV2, ResNet50V2, ResNet50V1, DenseNet-201, and ResNet11. The Majority Voting gave us an average accuracy of 0.99314 with 100% accuracy regarding the COVID class when tested on the test and the validation set. To avoid the classifiers' performance change from one test set to another, we propose the Majority Voting as the best strategy to follow in general cases. This study emphasizes more on the utility of the Majority Voting in treating sensitive and important tasks, like COVID-19 diagnosis.  
In future work, we need to invest more in the voting approaches by studying its performance on larger datasets. Moreover, we need to go deeper in studying the soft approach as it gives better results than the hard approach in many cases. Finally, we need to overcome the cases where true labels are voted by a minority to tune the Majority Voting to better performance.  
[PERSON], [PERSON], and [PERSON] designed the method. [PERSON] and [PERSON] implemented the method and wrote the paper. [PERSON], [PERSON], and [PERSON] contributed to the supervision of the work, analysis of the method, and paper writing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Robotics and Internet of Things Lab of Prince Sultan University.  
The data presented in this study are available on request from the corresponding author.  
We want to acknowledge The Robotics and Internet of Things Lab of Prince Sultan University for funding.  
The authors declare no conflict of interest.