Respiratory diseases remain major global health challenges, and traditional auscultation is often limited by subjectivity, environmental noise, and inter-clinician variability. This study presents an explainable multimodal deep learning framework for automatic lung-disease detection using respiratory audio signals. The proposed system integrates two complementary representations: a spectral-temporal encoder based on a CNN-BiLSTM Attention architecture, and a handcrafted acoustic-feature encoder capturing physiologically meaningful descriptors such as MFCCs, spectral centroid, spectral bandwidth, and zero-crossing rate. These branches are combined through late-stage fusion to leverage both data-driven learning and domain-informed acoustic cues. The model is trained and evaluated on the Asthma Detection Dataset Version 2 using rigorous preprocessing, including resampling, normalization, noise filtering, data augmentation, and patient-level stratified partitioning. The study achieved strong generalization with 91.21% accuracy, 0.899 macro F1-score, and 0.9866 macro ROC-AUC, outperforming all ablated variants. An ablation study confirms the importance of temporal modeling, attention mechanisms, and multimodal fusion. The framework incorporates Grad-CAM, Integrated Gradients, and SHAP, generating interpretable spectral, temporal, and feature-level explanations aligned with known acoustic biomarkers to build clinical transparency. The findings demonstrate the framework's potential for telemedicine, point-of-care diagnostics, and real-world respiratory screening.

翻译：呼吸系统疾病仍是全球重大健康挑战，传统听诊方法常受限于主观性、环境噪声及临床医师间差异。本研究提出一种基于呼吸音频信号的可解释多模态深度学习框架，用于肺部疾病自动检测。该系统整合了两种互补表征：基于CNN-BiLSTM Attention架构的谱时编码器，以及捕获MFCCs、谱质心、谱带宽和过零率等具有生理意义描述符的手工声学特征编码器。通过后期融合策略结合这两个分支，以充分利用数据驱动学习与领域知识引导的声学线索。研究采用严格预处理流程（包括重采样、归一化、噪声滤波、数据增强及患者级分层划分）在Asthma Detection Dataset Version 2数据集上进行训练与评估，实现了91.21%准确率、0.899宏平均F1分数和0.9866宏平均ROC-AUC的强泛化性能，优于所有消融变体。消融实验证实了时序建模、注意力机制与多模态融合的重要性。该框架集成Grad-CAM、Integrated Gradients和SHAP方法，生成与已知声学生物标志物一致的可解释谱图、时序及特征级分析，增强临床透明度。研究结果证明了该框架在远程医疗、床旁诊断及现实世界呼吸筛查中的应用潜力。