## 3 Experimental Results  
The proposed PCR-DCNN model for COVID-19 detection demonstrated fast convergence during the training process, in which the model fully converged at epoch 6, as shown by the accuracy and loss training curves in Figure 4. The final obtained model fits the data well, which can be clearly seen in the loss curves of both the training and validation data (see Figure 4b), where both curves converge with each other through the training process and also yield a very low loss (i.e., near zero). The performance of the obtained model was verified using an unseen test dataset for the final generalization performance. As shown in the generated confusion matrix in Figure 5, all four classes were predicted with 100% accuracy, and all the statistical analyses (\(ACC\), \(F_{1}\) score, \(MCC\), \(TPR\), and \(TNR\)) were reported to be 1 for each class prediction. Importantly, the rejection success of the network where the \(TNR\) outcome is 1 indicated that the network did not have any indecision when predicting the diagnostic class of the corresponding input HEX and FAM images of the patients. Similar results were also obtained when analyzing the ROC curve given in Figure 6, in which the proposed model separated the positive and false classes accurately; hence, the AUC value was 1. Figure 4: (a) Accuracy and (b) loss curves for COVID-19 diagnosis classification. Furthermore, the MobileNetV2 model was compared with other well-known pre-trained models. In this comparison, 5-fold cross-validation was carried out by combining both training and validation sets, that is, a total of 400 patient samples; thus, samples were divided into five equal sets, where each set contained 80 patient samples. It is important to note that each split set also contained the same number of input images from each class, that is, 20 patient input images from each diagnosis class composed of one set of 5-fold cross-validation process. This ensures that the trained models are less prone to overfitting [35]. The current DCNN model was trained 5 times by leaving one split set (or fold) out to test the performance of the model while using the other four sets for model training. In every training session, new parameters of the model were initialized. Finally, the average \(ACC\), \(F_{1}\) score, \(MCC\), \(TPR\), \(TNR\) and \(AUC\) were reported for the corresponding model. The resultant confusion matrices obtained with 5-fold cross-validation for each model are shown in Figure 7. As can be seen clearly in the models' confusion matrix, the models predicted a few incorrect classes, mostly the class of negative diagnoses when the samples were actually positive results in COVID-19. This is because when the viral load in the patient sample is low, the magnitude of the fluorescence signal can be weak in the FAM channel, which causes confusion between negative and positive classes; however this situation can be overcome when more of this type of sample data is presented to the network during training. For each pre-trained model, Table 2 shows the average performance measure and standard deviation obtained from 5-fold cross-validation. Here, the highest \(MCC\) and \(F_{1}\)-score of the model were considered as the best model. Consequently, although the performances of all  
Figure 5: Confusion matrix obtained for multi-input MobileNetV2 model on 160 (patient) test samples. The upper number in each entry of the matrix is the total number that the model predicts for the corresponding classes, and the bottom number is the percentage, which refers to the total number of test samples. Figure 6: ROC curves demonstrate the probability curve between true positive rate and false positive rate. Note that the curves of all classes are the same and overlap on the figure. models were similar, the performances of MobileNetV2 and DenseNet201 were slightly better than those of the other models.