# COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting
## 3 Results
### Performance Evaluation and Metrics  
For the evaluation of our proposed algorithms, we used six metrics based on the following parameters:  
* **True Positives (TP):** It represents the number of images belonging to a class \"X,\" and the model predicts correctly that they belong to the class \"X\". For example, the input image is of class \"Normal\" and the model predicts correctly that it is of class \"Normal\". * **True Negatives (TN):** It represents the number of images that do not belong to a class \"X\" and the model predicts correctly that they do not belong to the class \"X\". For example, the input image is not \"COVID\", and the model predicts correctly that it is not of the class \"COVID\". * **False Positives (FP):** It represents the number of images belonging to a class \"X\" and the model falsely predicts that it belongs to another class different from \"X\". For example, the input image is \"COVID\" and the model falsely predicts it as \"Normal\". * **False Negatives (FN):** It represents the number of images that do not belong to a class \"X\" and the model falsely predicts that they belong to the class \"X\". For example, the input image is not \"COVID\", and the model predicts it falsely as \"COVID\". The batch size is a hyperparameter of gradient descent representing the number of training samples fed to the network in one iteration before updating its parameters. Its value depends on the size of the model, the GPU memory, and the convergence of the results. The four metrics used for the evaluation are:  
\[\textbf{Accuracy}=(TP+TN)/(TP+TN+FP+FN), \tag{5}\]  
\[\textbf{Precision}=TP/(TP+FP), \tag{6}\]  
\[\textbf{Recall}=TP/(TP+FN),\textit{ and} \tag{7}\]  
\[\textbf{FIscore}=\frac{2*Precision*Recall}{(Precision+Recall)}. \tag{8}\]  
For our problem: the COVID-19 diagnosis in chest X-ray images, all these defined metrics have a significant meaning and interpretation. The accuracy measures the degree of right predictions among the total predictions (right and false) of the model. We will consider during our study the overall accuracy of the model to be able to judge the global performance of the model overall the classes. Concerning the precision, the recall, and the F1 score, we will consider only the class \"COVID\". This is more fruitful for our study and analysis. The precision will reflect the model's ability to only detect \"COVID\" cases without falsely classifying images that belong \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Learning** & **Batch** & **Epochs Number of** & **Number of** \\  & **Rate** & **Size** & **Best Convergence** & **Epochs** \\ \hline MobileNetV2 & 1.00 \(\times 10^{-5}\) & 200 & 760 & 1000 \\ Xception & 1.00 \(\times 10^{-5}\) & 100 & 471 & 500 \\ InceptionV3 & 1.00 \(\times 10^{-5}\) & 100 & 369 & 400 \\ DenseNet-201 & 1.00 \(\times 10^{-5}\) & 64 & 270 & 300 \\ VGG16 & 1.00 \(\times 10^{-4}\) & 64 & 169 & 200 \\ ResNet50V1 & 1.00 \(\times 10^{-5}\) & 100 & 152 & 200 \\ ResNet50V2 & 1.00 \(\times 10^{-5}\) & 100 & 231 & 300 \\ ResNet11 & 1.00 \(\times 10^{-5}\) & 100 & 425 & 500 \\ EfficientNet-B0 & 1.00 \(\times 10^{-4}\) & 32 & 224 & 300 \\ EfficientNet-B1 & 1.00 \(\times 10^{-5}\) & 32 & 152 & 200 \\ EfficientNet-B2 & 1.00 \(\times 10^{-5}\) & 32 & 280 & 300 \\ EfficientNet-B3 & 1.00 \(\times 10^{-5}\) & 32 & 373 & 400 \\ EfficientNet-B4 & 1.00 \(\times 10^{-5}\) & 32 & 350 & 400 \\ EfficientNet-B5 & 1.00 \(\times 10^{-5}\) & 32 & 415 & 500 \\ EfficientNet-B6 & 1.00 \(\times 10^{-5}\) & 16 & 252 & 300 \\ EfficientNet-B7 & 1.00 \(\times 10^{-5}\) & 16 & 290 & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trainig parameters of the deep learning models. to other class as \"COVID\" cases. The clinical impact of low precision is the increase in the number of classifying safe people (Normal and Viral Pneumonia cases) as COVID. It will engender more false alarms and add more surcharge for the COVID medical staff as they will attentively care for persons that did not suffer from COVID.