## 5 Conclusions and Future Work  
In summary, the advanced deep learning model, which employs the DenseNet201 architecture, along with the Mish activation function and multi-scale convolutions, demonstrates impressive performance in diagnosing pediatric pneumonia from chest X-rays. Trained on a dataset comprising 5856 images, it achieved an overall accuracy of 96.42%, thereby securing its position among the top methodologies within this field. However, its performance not only exceeds that of numerous ensemble-based techniques, but it also retains a streamlined and computationally efficient design. This architectural efficiency is particularly advantageous for deployment in resource-constrained environments because ensemble methods can often be impractical due to their inherent complexity and computational requirements. The model's ability to provide timely and accurate diagnoses offers a scalable solution for addressing diagnostic gaps in low- and middle-income countries, where pneumonia remains a leading cause of childhood mortality, contributing to over 740,000 deaths annually. By reducing reliance on radiologists and advanced diagnostic infrastructure, the model has the potential to enhance healthcare delivery in under-resourced regions. Despite its strengths, limitations include dependency on high-quality datasets, potential biases in training data, and challenges in generalizing across diverse populations. Furthermore, epidemiological and demographic distributions in LMICs may differ significantly from the public datasets used in this study. This limitation highlights the need for further validation using real-world and diverse demographic data to ensure broader applicability and reliability of the model. Future work could also explore multi-class tasks, such as distinguishing between bacterial and viral pneumonia, to enhance diagnostic precision and clinical relevance. To address these limitations, we aim to incorporate additional imaging modalities and integrate the model into clinical decision support systems. Scalability for other modalities, such as ultrasound, represents an important opportunity for expanding the model's utility across different healthcare contexts. Additionally, cloud-based deployment strategies could enhance scalability, enabling real-time analysis and continuous model refinement to adapt to evolving clinical needs. This approach represents a significant step forward in leveraging artificial intelligence for equitable and effective healthcare delivery. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a18020098/s1](https://www.mdpi.com/article/10.3390/a18020098/s1)]([https://www.mdpi.com/article/10.3390/a18020098/s1](https://www.mdpi.com/article/10.3390/a18020098/s1)), Table S1. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update [51]. Conceptualization, P.R. ; methodology, P.R. ; software, P.R. ; validation, D.R. and G.M. ; formal analysis, P.R. ; investigation, P.R. ; resources, P.R. ; data curation, P.R. ; writing--original draft preparation, P.R. ; writing--review and editing, P.R., D.R. and G.M. ; visualization, P.R. ; supervision, G.M. ; project administration, G.M. ; funding acquisition, D.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The code developed in this study is available upon request from the corresponding author. The open access Chest X-ray Images (Pneumonia) repository containing images collected from pediatric patients, is divided into two categories (Pneumonia/Healthy) and is available at [[https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)]([https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)) (accessed on 12 December 2024). **Conflicts of Interest:** Author [PERSON] was employed by the company \"Layer d.o.o.\".