Cross-Attention Transformer Networks with Optimized Feature Selection for Explainable Respiratory Disease Classification - Bajes Zeyad Aljunaeidi; Mohammed Tawfik; Issa M. Alsmadi; Yasser Mohammad Al-Sharo (Statistics, Optimization & Information Computing, 2025)
Respiratory diseases require accurate diagnosis for effective treatment, yet traditional methods rely on subjective assessments and expensive procedures. This paper presents a transformer-based cross-attention framework for acoustic respiratory disease classification with explainable AI integration. The CrossAttentionAcousticNetwork combines CNN spectral feature extraction with transformer temporal modeling, enhanced by cross-attention mechanisms for multi-modal feature fusion. Namib Beetle Optimization selects discriminative features from 100-dimensional handcrafted and deep spectral representations, while LIME provides clinical interpretability. Evaluation on ICBHI 2017 and KAU datasets achieves 99.0% and 95.0% accuracy respectively, representing 21.39% improvement over existing methods. The framework demonstrates superior performance across asthma, COPD, pneumonia, and heart failure classifications while maintaining computational efficiency for real-time deployment. Integrated explainable AI reveals clinically relevant acoustic patterns, with data augmentation improving minority class recognition by 19%. This approach bridges the gap between high-performance deep learning and clinical transparency requirements for automated respiratory disease screening.