## 4 Results and Discussion
To further validate the model, enhance interpretability, and facilitate clinical integration, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed. Grad-CAM generates visual explanations by producing heatmaps that highlight the image regions most salient to the model's classification. In our architecture, this technique was applied to the last convolutional layer to ensure that the extracted features were both spatially and semantically rich. This technique is particularly valuable in the context of pediatric pneumonia diagnosis, as it offers transparency into the model's decision-making process, enabling verification of its diagnostic rationale and fostering clinical trust. In healthy cases, Grad-CAM typically generates diffuse activation patterns across the lung fields, consistent with the absence of focal pathology. Conversely, in cases of viral pneumonia, Grad-CAM heatmaps demonstrate more distributed and heterogeneous activation, often encompassing perililar regions and reflecting the characteristic interstitial infiltrates associated with viral infection. In contrast, bacterial pneumonia exhibits more localized and intense activation patterns, concentrated over regions of consolidation, aligning with the typical lobar presentation of bacterial infection. The results are presented in Figure 4.  
The proposed approach yielded an accuracy of 0.9642, placing it among the highest reported accuracies in this domain. A comparative evaluation is provided in Table 2. This result is slightly higher than the accuracy achieved by [PERSON] and [PERSON] [42], who reported an accuracy of 0.9626 using their SGDRE method on a similarly sized dataset. Other studies, such as [PERSON] et al. [43] using the LeNet-5 model on a significantly larger dataset of 84,484 images, reported an accuracy of 0.9600, which is comparable to ours despite the larger dataset. Notably, many methods using ensemble architectures, such as  
Figure 3: The confusion matrix for the proposed methodology. The classified values are represented by the upper numerical entry in each cell, with the corresponding class-specific percentage shown below. [PERSON] and [PERSON] [24], who reported an accuracy of 0.9503, and [PERSON] et al. [44], who reported an accuracy of 0.9391, achieved slightly lower accuracy values despite leveraging advanced combinations of models. This suggests that our single-model approach provides state-of-the-art performance without requiring the complexity of ensemble methods. In terms of the F1 score, our approach achieved 0.9640, outperforming most other methods that explicitly reported this metric. For example, [PERSON] et al. [29], employing a custom CNN with a dataset of 5857 images, achieved an F1 score of 0.9430, while [PERSON] et al. [44] reported an F1 score of 0.9343 with their ensemble method on a dataset of the same size. Notably, [PERSON] et al. [43] and [PERSON] [45] also achieved F1 scores of 0.9600 using LeNet-5 and MobileNetV3, respectively, with larger datasets. These comparisons highlight the efficiency of our approach, which performs competitively with methods that use significantly more training data. Some studies, such as [PERSON] et al. [46], did not report F1 scores, focusing only on accuracy. This omission is concerning, as accuracy alone does not provide insight into the balance between false positives and false negatives, which is particularly important in medical diagnostics where class imbalances are common. By not reporting F1 scores, these studies risk underestimating the importance of false negative rates, which can have critical clinical implications. Reporting the F1 score, on the other hand, ensures a more comprehensive evaluation of a model's performance in such sensitive applications. Our approach, based on the improved DenseNet201 model with multi-scale convolutions and the Mish activation function, achieved superior metrics compared to both custom CNNs and hybrid combinations of CNNs with other methods. For instance, our accuracy of 0.9642 surpasses the performance of [PERSON] et al.