SwinMLP-XAI: An Explainable Swin Transformer-Based Framework for Automated Detection of Tuberculosis in Chest Radiographs

This study introduces an explainable deep learning framework (SwinMLP-XAI) using a Swin-Tiny Transformer backbone to automate tuberculosis detection. It overcomes existing internal validation and interpretability issues by incorporating Integrated Gradients for clear attribution mapping, achieving 99.84% classification accuracy.