Attention-guided feature importance-powered chronic obstructive pulmonary disease screening using lung sounds

Chronic obstructive pulmonary disease (COPD) leads to progressive airflow obstruction, impairing respiratory function. In this study, a deep-learning framework is developed to analyze lung sounds (LS) and support COPD screening using an explainable approach. In the proposed methodology, the LS signals are transformed into spectrograms, highlighting crucial acoustic features. Convolutional neural networks (CNNs) and vision transformers (ViTs) models are introduced to extract diverse features, enhancing feature representation. Subsequently, an attention-guided feature importance (AGFI) is employed to refine the features. An ensemble architecture leveraging TabNet and Support Vector Machine (SVM) with radial basis function (RBF) as base learners and Neural Oblivious Decision Ensembles (NODE) as a meta-learner is proposed to classify features into COPD and normal classes. Experimental outcomes validated on internal and external datasets demonstrate the model’s exceptional classification performance. The proposed model achieves outstanding generalization performance on the external dataset with an accuracy of 96.31% and an F1-score of 96.55%, outperforming the traditional ensemble methods. Through the innovative artificial intelligence-powered COPD screening approach, the shortcomings of conventional screening approaches are addressed. The feature-extraction architecture and ensemble classification enable the proposed model to provide a scalable, explainable, and clinically reliable solution across diverse healthcare settings.