Pneumonia remains a major global health concern, and chest X-ray imaging is one of the most common diagnostic tools for its detection. This paper presents an experimental comparison of deep learning models applied to pneumonia classification in chest X-ray datasets. The authors evaluate several convolutional neural network (CNN) architectures and training strategies, analyzing their performance in terms of accuracy, sensitivity, and robustness. The results provide insights into the trade-offs between model complexity and diagnostic reliability, offering practical guidance for deploying AI-driven medical imaging tools in healthcare settings.

(DOI not yet available — paper in press)