# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 6 Discussion  
The recent statistical data from WHO and CDC has shown a significant increase in COVID-19 cases, hospitalizations, and deaths in the first week of January 2022. In the US, 98% of cases are caused by the spread of the Omicron variant of the Coronavirus [49]. Therefore, the early prediction of COVID-19 is of immense importance to help in avoiding the disease spread and thus protecting immune-vulnerable people. As in many other diseases, early detection can be very helpful for diagnosis and evaluation, as well as for follow-up on disease progression. Thus, an automated system that utilizes patient data could be a valuable decision support tool for COVID-19 detection and provide significant downstream treatment/follow-up implications. For that purpose, we have developed a deep learning system (DLS) to predict COVID-19. The proposed DLS is validated experimentally on a benchmark dataset of chest X-ray images, which contains three classes: COVID-19, normal control, and pneumonia. The proposed pipeline utilized a pre-trained Xception model and modified the network structure by including the global average pooling layer. Then, we used the layer to solve the vanishing gradient problem and reduce the probability of overfitting; the activation layer was used for reducing losses. Furthermore, we have explored different activation functions at different thresholds to improve performance and reduce the losses of the proposed pipeline. Additionally, extensive evaluation has been conducted using different optimizers. Developing an analysis pipeline with high accuracy is our ultimate goal, for which various experiments have been conducted to evaluate the performance using various evaluation metrics, such as precision, recall, accuracy, F1-score, and area under the ROC. Before performing any experiments, data augmentation (i.e., rotation by 25 degrees, a zoom range equal to 0.2 and fill mode set to nearest) has been applied before feeding the data into the DLS to overcome the dataset imbalance. Experimental results have revealed that the proposed DLS performance can be improved by carefully employing a nonlinear activation function and an optimization method. Particularly, the proposed model achieved an accuracy of 99.3% and a minimum loss of 0.02 using the combination of the RMSprop optimizer and LeakyReLU with \(\alpha=0.1\). Other optimization techniques demonstrated closer results over shorter times (e.g., AdaMax). This has been documented using overall and class-wise accuracies, ROC curves and confusion matrices. Similar experiments were conducted using the ELU activation at \(\alpha=0.2\). As  
Figure 10: ROC curves of the pre-trained models with learning rate \(\eta=0.1\), ReLU activation function, and Adam optimizer. (**a**) VGG16, (**b**) Xception, (**c**) DenseNet121, (**d**) InceptionV3, (**e**) ResNet50, and (**f**) MobilNet. Figure 7 showed, the COVID-19 class samples (i.e., 130) were correctly classified, while six normal cases resulted in negative and positive prediction errors. Overall, the LeakyReLU activation function at a threshold of \(\alpha\) equal 0.1 combined with the RMSprop optimizer is the best case (see Tables 2 and 3 and Figures 5 and 6). It is worth mentioning that, additionally, the use of TL helped our model to start from good initial parameters, and few training epochs were needed to achieve better performance. Particularly, during model training, the loss/accuracy curves for the training vs validation showed that the validation accuracy became high with small losses after a few epochs (=10). Additional comparisons between the developed DLS and other pre-trained deep learning-based methods, including Inception V3, DenseNet121, ResNet50, MobilNet, and VGG16, have been conducted. We unified the parameters for all systems by using the ReLU activation function, \(\eta\) = 0.1, and the Adam optimizer.