## 2 Related Works  
Although deep learning has made significant progress in advancing automated pneumonia diagnosis, achieving higher performance and ensuring the scalability and robustness of these models in real-world clinical settings remain ongoing challenges. Previous studies have demonstrated moderate success in refining deep learning techniques for detecting pneumonia from medical imaging data. [PERSON] and [PERSON] [24] evaluated the performance of an ensemble model combining three CNN architectures (InceptionResNetV2, DenseNet201, and VGG16) for binary pneumonia classification using 5856 chest X-ray images. Compared to single CNN models, the ensemble demonstrated superior performance, achieving an accuracy of nearly 0.95 and improving the average F1 score by 3%. Additionally, the study underscores the potential of ensemble methods to outperform individual architectures in medical image analysis tasks. [PERSON] and [PERSON] [25] emphasized the importance of rapid COVID-19 diagnosis due to its widespread social and economic impact and the limitations of molecular tests. Their study utilized six CNN architectures (VGG19, InceptionV3, ResNet50, InceptionResNetV2, and DenseNet121) to classify chest CT images as COVID-19 or non-COVID-19, comparing the effects of various activation functions. They found that InceptionV3 and DenseNet121 performed best when paired with non-ReLU activation functions, highlighting their potential to improve diagnostic accuracy in resource-limited areas. [PERSON] et al. [26] highlighted pneumonia as a leading cause of mortality in children under five and emphasized the importance of chest X-ray imaging for its diagnosis. In research, they used transfer learning with the ImageNet pre-trained Xception Network to classify chest X-rays, distinguishing between 3883 pneumonia cases and 1349 normal images. The model demonstrated competitive performance compared to state-of-the-art methods, achieving a precision of 0.84, a recall of 0.99, an F1 score of 0.91, and an AUC of 0.97. [PERSON] et al. [27] highlighted pneumonia as a leading cause of death among children, with 880,000 deaths reported in 2016, particularly in South Asia and Sub-Saharan Africa. Using CNNs, the researchers classified chest X-ray images to classify pneumonia. They experimented with different parameters, hyperparameters, and the number of convolutional layers within the CNN architecture. Two custom CNN models achieved validation accuracies of 0.8526 and 0.9231, while pre-trained models like VGG16, VGG19, ResNet50, and InceptionV3 attained accuracies of 0.8728, 0.8846, 0.7756, and 0.7099, respectively. [PERSON] [28] emphasized the importance of rapid pediatric pneumonia detection due to its seasonal association and potentially fatal outcomes. The study utilized deep tuning transfer learning of state-of-the-art CNN models, specifically DenseNet121, achieving an accuracy of 0.9503 and an F1 score of 0.9603 on a dataset of 5856 chest X-ray images. The results demonstrated the effectiveness of the approach for accurate and timely pneumonia detection in pediatric cases. [PERSON] et al. [29] proposed a DenseNet-based method with a Squeeze and Excitation (SE) block and max-pooling to enhance pneumonia classification by better focusing on lesion regions. They selected PReLU as the activation function to prevent neuron death during training. The model outperformed DenseNet, achieving 0.928 accuracy, 0.926 precision, 0.962 recall, and 0.943 F1 score, with improvements in all metrics. Previous studies on pediatric pneumonia recognition using CNNs face limitations, including inadequate feature extraction for detecting subtle patterns in chest X-rays. For example, while ensemble models combining architectures like DenseNet201, InceptionResNetV2, and VGG16 demonstrated superior performance with improved F1 scores [24], their computational complexity makes them less practical for resource-constrained environments.