Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method

2024/07/15 by Yi-Wei Chua, Chua, Yi-Wei, Yun‐Chien Cheng +1
Medicine · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Chronic Obstructive Pulmonary Disease (COPD) Research #FOS: Computer and information sciences #FOS: Electrical engineering #Phonocardiography and Auscultation Techniques #Respiratory and Cough-Related Research #Sound (cs.SD) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2407.10828

openalex publication_date 2024/07/15 · openalex created_date 2024/07/17 · openalex updated_date 2026/07/28

Abstract

This study aims to develop an auxiliary diagnostic system for classifying abnormal lung respiratory sounds, enhancing the accuracy of automatic abnormal breath sound classification through an innovative multi-label learning approach and multi-head attention mechanism. Addressing the issue of class imbalance and lack of diversity in existing respiratory sound datasets, our study employs a lightweight and highly accurate model, using a two-dimensional label set to represent multiple respiratory sound characteristics. Our method achieved a 59.2% ICBHI score in the four-category task on the ICBHI2017 dataset, demonstrating its advantages in terms of lightweight and high accuracy. This study not only improves the accuracy of automatic diagnosis of lung respiratory sound abnormalities but also opens new possibilities for clinical applications.

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