<![CDATA[ Pneumonia remains one of the leading causes of mortality among children worldwide, particularly in developing countries, where early and accurate diagnosis is crucial. This study aims to evaluate and compare the performance of three deep learning architectures, namely VGG16, MobileNetV2, and ResNet50, for pediatric pneumonia classification using chest X-ray images. The dataset consists of pediatric chest radiographs (ages 1-5 years) obtained from Guangzhou Women and Children’s Medical Center, which were preprocessed through normalization and data augmentation techniques to improve model generalization. The classification task involves three categories: normal, bacterial pneumonia, and viral pneumonia. Model performance was evaluated using accuracy, precision, recall, specificity, F1-score, G-Mean, and AUC. The experimental results show that all models achieve competitive performance, with accuracy ranging from 79% to 82%, where VGG16 outperforms the other architectures. Furthermore, Grad-CAM is applied to enhance interpretability by visualizing important regions in X-ray images that influence model decisions. The results demonstrate that Grad-CAM provides meaningful visual explanations, supporting the reliability of deep learning models in assisting clinical diagnosis.]]>
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