Automated Pneumonia Detection Using Dual-Architecture Deep Learning with GhostNet and Mo-bileNetV2

Abstract

Pneumonia remains a significant global health concern, especially in regions with limited medical resources, underscoring the need for accurate, efficient, and interpretable diagnostic solutions. This study proposes a dual-architecture deep learning framework combining GhostNet and MobileNetV2 for automated pneumonia detection using chest X-ray images. The model leverages GhostNet’s efficient feature extraction and MobileNetV2’s lightweight precision, integrated via a custom concatenation layer to enhance performance while maintaining computational efficiency. Training was conducted on a publicly available pediatric chest X-ray dataset comprising 5,872 images from the Guangzhou Women and Children’s Medical Center. A patient-level split of 70% for training, 15% for validation, and 15% for testing was used, ensuring no data leakage across subsets. Although cross-validation was not applied, generalizability was assessed on an external adult dataset from Indiana University (Open-i), with the model achieving 85% test accuracy and 87% validation accuracy. On the internal test set, the proposed method attained 97.45% accuracy, 99.82% precision, 96.74% recall, and a 98.25% F1-score, outperforming established models such as ResNet50, VGG16, and EfficientNet. Training and validation loss curves showed minimal divergence, and Grad-CAM visualizations offered interpretability by highlighting salient lung regions influencing predictions. The lightweight and adaptable nature of the model makes it particularly suitable for real-world deployment in resource-constrained healthcare environments. Future work will focus on expanding the dataset, adopting k-fold cross-validation, integrating continual learning strategies, conducting subgroup and fairness analyses, and exploring explainable AI tools to further enhance clinical applicability and trust.