A Hybrid and Explainable Deep Learning System for Chest X-Ray Disease Classification with Flask-Based Web Interface

This research contributes HybridXNet, an explainable hybrid CNN-XGBoost framework for multi-class chest X-ray disease classification. By integrating pretrained CNNs with XGBoost and Grad-CAM, the proposed system achieves strong diagnostic performance while providing visual explanations of predictions. The development of a Flask-based application further demonstrates its practical potential for transparent and accessible AI-assisted disease screening.