## 4 Results
### CovidDetNet Experimental Setup
#### 4.3.1 Performance Evaluation  
This experiment aims to validate the COVID-19 detection (three-class classification) performance of the proposed CovidDetNet framework using chest radiograph images. For this experiment, the dataset is divided into training and testing sets, i.e., we used 70% of the data for model training and 30% for testing. More specifically, we used all the 15,153 radiograph images (3616 data instances of COVID-19 patients, 1345 pneumonia radiographs, and 10,192 instances of healthy individuals) of the dataset named COVID-19 Radiography Database, where 10,606 images (2531 images of COVID-19 individuals, 941 pneumonia radiographs, and 7134 images of healthy individuals) were used for training and the remaining 4547 images (1085 images of COVID-19 individuals, 404 Pneumonia radiographs, and 3058 images of healthy individuals) for testing. The training set trains the proposed CovidDetNet framework for COVID-19 detection and classification with the same parameters mentioned in Table 2. The training of the proposed CovidDetNet model consumed 3853 min and 11 s for COVID-19 detection and classification. This time, however, is proportional to the maximum number of epochs and iterations per epoch. The total number of iterations in the training stage for CovidDetNet is 4920 (82 iterations per epoch), and the number of epochs is 60. At epoch 60, the model achieved an average validation accuracy of 98.40%. To assess the training performance of our approach we have shown accuracy and loss in Figures 4 and 5. Figure 4: Training and validation accuracy of CovidDetNet model (black line shows testing accuracy whereas blue line shows training accuracy). The loss function indicates how well the framework can predict the dataset. The loss and accuracy of our model after epoch 47 approximately remains the same, which shows that our model is predicting COVID-19 with higher accuracy even at lower epochs than 60. The training and validation process of the proposed CovidDetNet approach is shown in Figure 4 and the confusion matrix for the testing phase of the CovidDetNet framework for COVID-19 infection detection is shown in Table 4. The proposed CovidDetNet architecture misclassified 109 radiographs out of 4547, of those, 48 COVID-19 images, 50 images for normal, and 11 images for viral pneumonia. The proposed CovidDetNet framework has higher TN and TP values, as well as lower FN and FP values, as shown by the confusion matrix. As a result, the suggested method is capable of accurately classifying COVID-19 situations. In addition, the training epochs in the experiments are changed from 20 to 60 with a step size of 10 to ensure the validity of the obtained results. Table 5 shows the accuracy, precision, recall, F1-score, and kappa values obtained by varying different values of epochs. The best results in terms of all performance measures are obtained with 60 training epochs. It is concluded that the accuracy of the proposed approach increases gradually with the increase in the number of epochs. The proposed CovidDetNet approach attained the average accuracy, precision, recall, specificity, F1-score, and Kappa index of 98.40%, 97.0%, 96.66%, 97.06%, 96.82%, and 95%, demonstrating its reliability in COVID-19 detection, as shown in Table 5. Figure 6 shows the receiver operating characteristic (ROC) curve of the proposed CovidDetNet framework. The area under the curve is 0.9955. Similarly, Figure 7 demonstrates the box plot of the proposed CovidDetNet approach with various values of epochs. It is demonstrated that increasing the size of epochs improves the values of various evaluation matrices, as shown in Table 5. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\ \hline \multirow{3}{*}{Actual class} & & COVID-19 & Normal & Viral Pneumonia \\  & COVID-19 & 1037 & 46 & 2 \\ \cline{1-1}  & Normal & 36 & 3008 & 14 \\ \cline{1-1}  & Viral Pneumonia & 3 & 8 & 393 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of the proposed CovidDetNet model.