## 1 Introduction
This capability not only enhances diagnostic efficiency; however, it also serves as a valuable supplementary tool for clinicians--minimizing the risk of missed diagnoses and contributing to improved patient outcomes [18]. Although these advancements are significant, they still require ongoing refinement and validation in clinical settings because the stakes in medical diagnostics are exceptionally high. Despite the considerable advancements in deep learning applications within the realm of medical imaging, existing models exhibit numerous limitations. Many of the leading models--such as Inception, ResNet, and VGG--are computationally demanding, necessitating extensive memory and processing capabilities [19; 20]. These prerequisites render them difficult to implement on edge devices or in settings with limited resources [19]. Furthermore, these models frequently possess a vast number of parameters, which heightens the risk of overfitting when trained on small or imbalanced datasets; this scenario is particularly prevalent in medical imaging [21]. Another significant challenge pertains to the interpretability of deep learning models. Although these algorithms can achieve high accuracy, their opaqueness complicates clinicians' ability to trust and effectively integrate them into their practice [22; 23]. Addressing these limitations is crucial for facilitating the widespread adoption of deep learning techniques in pediatric pneumonia diagnostics. This study intends to tackle the deficiencies of current deep learning models by developing an enhanced DenseNet201-based model for pediatric pneumonia recognition. The primary aim of this study is to optimize the architecture of a deep-learning model for pediatric pneumonia diagnosis. This is accomplished by leveraging a pretrained DenseNet201 model and introducing multi-scale convolutional layers. These layers facilitate the model in capturing features at varying resolutions, thus enhancing its ability to identify pneumonia across diverse radiographic presentations. The model was evaluated using a publicly available dataset, which ensures transparency and reproducibility of the results. Furthermore, the Mish activation function was incorporated to provide smoother gradient propagation and improved convergence compared to traditional activation functions, such as ReLU. An essential aspect of the model's design is its parameter efficiency, achieved by minimizing the number of parameters in comparison to other pre-trained models. This design choice makes the model particularly suitable for deployment in resource-constrained environments where computational resources may be limited. However, by utilizing transfer learning and a publicly accessible dataset, we demonstrated the model's robustness and diagnostic accuracy across a variety of imaging scenarios. Although challenges remain, this approach significantly contributes to the field of pediatric pneumonia diagnosis. The primary contributions of this study are as follows:  
* This study leveraged a pretrained DenseNet201 model to develop a novel architecture optimized for pediatric pneumonia diagnosis. By introducing multi-scale convolutional layers, the model effectively captures features at varying resolutions, enhancing its diagnostic accuracy across diverse radiographic presentations. * The incorporation of the Mish activation function provides smoother gradient propagation and improved convergence compared to traditional activation functions, such as ReLU, further enhancing the model's stability and generalization capabilities. * An essential focus was placed on parameter efficiency. The model achieves a significantly lower parameter count compared to other pretrained architectures, ensuring computational efficiency and making it ideal for deployment in resource-constrained settings where advanced hardware is limited.