## 3 Results and Discussion
### Qualitative Evaluation  
CAMs are used to visualize what the network is focusing on when performing the classification, as introduced in Section 2.2.5. Figure 4 shows different input images and their corresponding CAM heatmaps obtained with COVID-XNet. The most relevant information that the network considered when performing the prediction for the COVID-19 class is highlighted in red, while regions that were not relevant for COVID-19 detection (considered as normal) are presented in dark blue.  
The examples that are shown in Figure 4 present different cases that correspond to true positives (A-H), true negatives (I-K) and false positives (L). The heatmaps obtained for the true positive cases were compared to the ground truth descriptions provided in the datasets in order to verify whether the system was highlighting the correct regions inside the lung area. It is important to mention that these results were also validated by a lung specialist.  
The ground truth corresponding to Figure 4A reports patchy ground-glass opacities in right upper and lower lung zones and patchy consolidation in the left middle to lower lung zones. Furthermore, several calcified granulomas were incidentally noted in the left upper lung zone. Figure 4B shows a right interstitial paracardiac thickening with a tendency to cavitation in its most cranial portion, along with a mild right hilar enlargement. Figure 4C presents consolidations in the base of the right hemithorax and an interstitial pattern that affects most of that lung. Moreover, a small pseudomodular  
Figure 4: CAMs obtained for the COVID-19 class together with their corresponding original images. Images (**A–H**) represent COVID-19 cases, while (**I–L**) correspond to healthy patients. CAMs are represented with heatmaps, where the most relevant regions for COVID-19 detection are highlighted in red.  
consolidation is presented in the left paracardiac region, which could suggest another affected area. In Figure 4D, the ground truth describes the presence of right upper lobe opacity. The report for Figure 4E details the existence of alveolar infiltrates in the right upper and lower lobe, and also in the left parahilar area. As can be seen in the corresponding heatmaps for these cases (A-E), relevant areas that were described in the ground truths were detected by the system. In the case shown in Figure 4F, the patient is reported to present opacities in the base of the right lung and in the left middle and lower lung zones. The output heatmap matches this description, along with a smaller region in the left upper area which is not mentioned in the report. Lower and middle to upper right lobe consolidations are reported in Figure 4G, together with a mild small consolidation in the left lower lobe. In this case, the system was not able to detect consolidations in the middle to upper right lung area. Finally, the ground truth of Figure 4H report COVID-19 pneumonia manifesting as a single nodular lesion. The AP chest radiograph shows a single nodular consolidation (black arrows) in the left lower lung zone. In the latter case, the system detected consolidations marked by ground truth arrows, but it also mistakenly highlighted upper areas in both lungs.  
For normal cases (I-L), the system did not detect any relevant COVID-19 area, except for Figure 4L, where two small regions were highlighted.  
These promising results prove that, even when training the system with a large unbalanced dataset obtained from different sources, our custom model is learning specific characteristics and patterns appropriately.