## 4 Results and Discussion  
The proposed model with the Mish activation function achieved notable performance in the diagnosis of pediatric pneumonia from radiological images, demonstrating an overall accuracy of 0.9642. A more comprehensive evaluation of accuracy assessment is presented in Table 1. This high accuracy indicates the model's effectiveness in correctly classifying both pneumonia-positive and healthy samples within the dataset, as reflected in the associated performance metrics. Furthermore, the table presented the results of the ablation study, comparing the Mish activation function against ReLU. This comparison provided the numerical evidence mentioned earlier, quantifying the impact of Mish on performance metrics and validating its effectiveness in this specific application. The F1 score was employed as a key evaluation metric due to its ability to provide a harmonic mean of precision and recall, particularly valuable in datasets characterized by class imbalance. During the research evaluation, it was concluded that accuracy is the most commonly used metric. We utilized it to enable an objective comparison with other studies. In addition to accuracy, the F1 score was also employed, although it was observed that many studies did not include this metric despite dealing with imbalanced datasets. To encourage future studies to adopt a fully objective approach to evaluating solutions, we emphasized the following metrics in our analysis: Accuracy, Precision, Recall (sensitivity), F1 score, and specificity, thereby ensuring a comprehensive and detailed evaluation. In this study, we employed a dataset consisting of 4273 chest X-ray images of pneumonia cases and 1583 images of healthy individuals. Despite the observed class imbalance, the model achieved a high F1 score of 0.9542, indicating a negligible impact of imbalance on the model's capacity for accurate classification. We extensively evaluated sensitivity and specificity to ensure accurate diagnoses, minimizing both missed cases and unnecessary treatments and ultimately improving patient outcomes. The model demonstrated a sensitivity of 0.9506, reflecting its ability to correctly identify pediatric patients with pneumonia while reducing the risk of missed or delayed diagnoses, which can have a serious impact on patient outcomes. Onwards, the model also demonstrated a high specificity of 0.9507, reflecting its ability to accurately classify healthy patients and to minimize false positives, thereby reducing unnecessary treatments such as inappropriate antibiotic use, which can lead to unwanted side effects and contribute to antibiotic resistance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Model with Mish** & **Model with ReLU** \\ \hline Accuracy & **0.9642** & 0.9616 \\ \hline Precision & **0.9580** & 0.9534 \\ \hline Recall (Sensitivity) & **0.9506** & 0.9489 \\ \hline F1 score & **0.9542** & 0.9511 \\ \hline Specificity & **0.9507** & 0.9489 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy assessment of the proposed approach. These findings are corroborated by the confusion matrix, which is shown in Figure 3. The model correctly identified 838 pneumonia-positive cases (true positives) and 292 healthy cases (true negatives) while misclassifying 25 healthy cases (false positives) and 17 pneumonia cases (false negatives). These outcomes align with the reported performance metrics, underscoring the model's reliability and effectiveness in pediatric pneumonia diagnosis. Further analysis of these misclassified instances suggested that some errors arose from cases exhibiting borderline radiological features or overlapping radiological characteristics with other pulmonary conditions, such as bronchitis or atelectasis. The findings suggest that augmenting the training dataset and implementing region-specific feature enhancement techniques could lead to a reduction in such misclassifications.