Explainable Deep Fusion Transfer Learning for Automated Lung Disease Classification in the U.S. Healthcare Environment

Abstract

Lung diseases continue to impose a significant healthcare burden in the United States, making early and accurate diagnosis essential for effective treatment and patient management. This study proposes an explainable fusion-based transfer learning framework for automated lung disease classification using chest X-ray and CT scan images. The proposed system integrates state-of-the-art deep learning architectures, including ResNet50, ResNet101, and EfficientNetB0, along with feature-level and decision-level fusion strategies to enhance classification accuracy and robustness. Experimental results demonstrate that the hybrid fusion models outperform standalone architectures, achieving 95.68% accuracy for chest X-ray classification and 99.61% accuracy for CT scan classification. To improve transparency and clinical reliability, Explainable Artificial Intelligence (XAI) methods such as SHAP, LIME, and Integrated Gradients were incorporated to visualize diagnostically relevant regions. The proposed framework offers an accurate, interpretable, and reliable AI-driven solution for pulmonary disease diagnosis in modern U.S. healthcare systems.