AI-Based Approach for Detecting Pediatric Pneumonia from Chest X-rays

Abstract

Pneumonia remains a significant health challenge for children, particularly in regions with limited diagnostic resources. Early and accurate detection is essential for timely treatment and improved clinical outcomes. This paper presents an automated deep learning (DL)-based framework for pediatric pneumonia detection (PPD) using chest X-ray images, designed to support clinical decision-making and reduce diagnostic workload. Five convolutional neural networks (CNNs) architectures: DenseNet121, MobileNetV2, VGG16, InceptionV3, and ResNet50 are trained and evaluated through extensive experimentation. Their performance is assessed using accuracy, precision, recall, and F1-score, alongside confusion matrices. Results demonstrate that DenseNet121 and MobileNetV2 achieved superior performance, with an accuracy of 93.75%, F1-scores of nearly 95.1 %, outperforming the other models in balancing sensitivity and specificity. VGG16 achieves competitive performance with high sensitivity, whereas InceptionV3 and ResNet50 exhibit limitations, particularly in terms of generalization and specificity. The system also demonstrates scalability potential, with MobileNetV2 offering lightweight deployment capabilities for low-resource environments. The findings confirm the clinical value of DL systems in supporting PPD and highlight the promise of DenseNet121 and MobileNetV2 as practical solutions for real-world healthcare applications.