Optimized Kolmogorov–Arnold Networks-Driven Chronic Obstructive Pulmonary Disease Detection Model

Across the globe, chronic obstructive pulmonary disease (COPD) continues to be a critical healthcare burden due to its progressive and undetected decline in pulmonary function. In recent times, convolutional neural networks (CNNs) and vision transformers (ViTs) have been widely applied to lung sounds (LS) analysis, leading to the development of improved non-invasive, accessible, and scalable COPD screening methods. However, the shortcomings of CNNs and ViTs architectures restrict the COPD classifier’s discriminative power in distinguishing normal and COPD samples. In this study, a unique and interpretable COPD classification is proposed. This study introduces a hybrid feature extraction strategy integrating CNNs and ViTs architecture to capture the crucial COPD patterns from the LS’s Mel-spectrogram representation. The proposed CNNs-ViTs architecture addresses the limitations of standalone CNNs or ViTs architectures by capturing fine-grained acoustic features and long-range temporal dependencies while efficiently modeling non-linear relationships. A novel feature fusion is used to combine the heterogeneous features into a unified and highly discriminative feature representation space. A tailored hyperparameter optimization strategy is developed to fine-tune the Kolmogorov-Arnold Networks (KANs) for classifying the features into normal and COPD classes. By incorporating internal (552 Healthy and 1324 COPD LS recordings) and external (35 Healthy and 77 COPD LS recordings) datasets, the authors evaluate the model’s performance. The model achieves a generalization accuracy of 97.62% and specificity of 96.51% on the external dataset. Through Shapley Additive Explanations (SHAP) overlays, the model’s interpretability is enhanced. The experimental findings highlight the effectiveness of the proposed COPD classification architecture in the domain of biomedical acoustic signal processing. Operating with minimal reliance on specialized diagnostic infrastructure renders the proposed model ideal for primary care and resource-limited environments.