基于深度学习的肺音分类研究

Research on Lung Sounds Classification Based on Deep Learning

肺部疾病对人类健康和生命安全有重大的影响,肺部出现异常是肺部疾病的直接反应,对肺音的研究在临床诊断中有重要的意义。随着电子听诊技术的不断发展,现代肺音的特征提取和分类技术也得到进一步研究。目前,肺音分类的研究是将信号分析和深度学习算法相结合,以提升辅助电子听诊器的实际应用。本文旨在对肺音的概念进行阐述,并分析肺音分类的发展现状以及肺音分类技术存在的不足,以期对肺音分类技术未来的研究方向和应用发展趋势进行展望。

Pulmonary diseases have a significant impact on human health and life safety,and abnormalities in the lungs are a direct response to lung diseases.The study of lung sounds is of great significance in clinical diagnosis.With the continuous development of electronic auscultation technology,the feature extraction and classification techniques of modern lung sounds have also been further studied.At present,the research of lung sound classification is to combine signal analysis with deep learning algorithm to improve the practical application of auxiliary electronic stethoscope.This paperaims to elaborate on the concept of lung sounds,analyze the current development status of lung sound classification,and discuss the shortcomings of lung sound classification technology.The goal is to an outlook on future research directions and application development trends in the field of lung sound classification.