# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 3 Results
### Discussion  
All the steps performed during this study have a remarkable impact on the efficiency of the classifiers. We started with the chest X-ray image pre-processing and the data augmentation. For image pre-processing, we proposed to remove all duplicate images from the original dataset (77 duplicate images were removed). Although the number of images has decreased in this case, it improves the dataset's performance because having duplicate images in the dataset creates a problem for two reasons. First, it introduces bias into your dataset, giving the deep neural network additional opportunities to learn patterns specific to the duplicates. Second, it hurts your model's ability to generalize to new images outside of what it was trained on. For the data augmentation, All of the pre-trained models were large enough to be overfitted easily on this dataset. To avoid this, we resized the images to 224 \(\times\) 224 \(\times\) 3, and we included the random rotation of the images, as data augmentation has an effect to prevent overfitting. Then, we selected the most powerful deep learning algorithms for the images classification to study in our task: MobileNetV2, Xception, InceptionV3, DenseNet-201, VGG16, ResNet50 (V1 and V2), ResNet11, and EfficientNet (B0, B1, B2, B3, B4, B5, B6, and B7), We fine-tuned these models by adding the proposed head model composed by one average pooling layer with a size of 4 \(\times\) 4, two ReLU activation layers, two batch normalization layers, dropout 0.5, and, finally, a Softmax layer. All proposed models demonstrated attractive results, with an accuracy rate of around 98%. Moreover, all the methods have 100% recall on the test set. A higher recall value means a lower number of False-Negative (FN) cases, which is very important in the diagnosis of COVID-19 cases. A patient who has a negative result is actually infected and will have a \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**AUC Scores Following the OVO Scheme**} & \multicolumn{2}{c}{**AUC Scores Following the OVR Scheme**} \\ \cline{2-5}  & **Macro Average** & **Prevalence-Weighted** & **Macro Average** & **Prevalence-Weighted Average** \\ \hline DenseNet-201 & 0.995056 & 0.993194 & 0.995056 & 0.993194 \\ ResNet50V1 & 0.999103 & 0.998765 & 0.999103 & 0.998765 \\ ResNet50V2 & 0.997229 & 0.996185 & 0.997229 & 0.996185 \\ MobileNetV2 & 0.999843 & 0.999783 & 0.999843 & 0.999783 \\ ResNet11 & 0.992285 & 0.989379 & 0.992285 & 0.989379 \\ Soft Majority Voting & 0.999827 & 0.999762 & 0.999827 & 0.999762 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: AUC scores for DenseNet-201, ResNet50V1, ResNetV2, MobileNetV2, ResNet11, and the Soft Majority Voting among them. normal life without taking any precautionary measures, which can cause the infection of other persons, which is very dangerous. We compared our results with state of the art tested on the original chest X-ray test dataset. [PERSON] et al. (2016) used COVID-Net network architecture, which has a lightweight residual (PEPX) design pattern. They obtained a sensitivity of 88.6% and a precision of 91.33%, with an accuracy of 92.4%. [PERSON] et al. (2019) used an implementation of the ResNet50 model, pretrained on the ImageNet dataset. They obtained a sensitivity of 96.9% and a precision of 96.8%, with an accuracy of 96.32%. The comparison is given in Table 9, and we can see that our best models have outperformed other state of the art methods. As demonstrated previously, the top selected classifiers gave us quasi-optimal results when tested on the chest X-ray test dataset. All of them gave 100% Accuracy in treating COVID cases, except the ResNet50V1. However, to be able to generalize, we need more experiments. This is why we tested all the classifiers and the Majority Voting again in another dataset, which is the Validation dataset. The results are provided in Table 10.  
From Table 10, we can deduce that the top 5 classifiers selected previously are among the best on the validation dataset.