Lung Sound Classification for Respiratory Disease Identification Using Deep Learning: A Survey

Thinira Wanasinghe, Sakuni Bandara, Supun Madusanka, D. Meedeniya, M. Bandara, Isabel De la Torre Díez
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引用次数: 0

Abstract

Integrating artificial intelligence (AI) into lung sound classification has markedly improved respiratory disease diagnosis by analysing intricate patterns within audio data. This study is driven by the widespread issue of lung diseases, which affect around 500 million people globally. Early detection of respiratory diseases is crucial for delivering timely and effective treatment. Our study consists of a comprehensive survey of lung sound classification methodologies, exploring the advancements made in leveraging AI to identify and classify respiratory diseases. This survey thoroughly investigates lung sound classification models, along with data augmentation, feature extraction, explainable techniques and support tools to improve systems for diagnosing respiratory conditions. Our goal is to provide meaningful insights for healthcare professionals, researchers and technologists who are dedicated to developing methodologies for the early detection of pulmonary diseases. The paper provides a summary of the current status of lung sound classification research, highlighting both advancements and challenges in the use of AI for more accurate and efficient diagnostic methods in respiratory healthcare.
利用深度学习进行肺部声音分类以识别呼吸系统疾病:调查
通过分析音频数据中错综复杂的模式,将人工智能(AI)融入肺部声音分类,明显改善了呼吸系统疾病的诊断。这项研究的动力来自于广泛存在的肺部疾病问题,全球约有 5 亿人受到肺部疾病的影响。呼吸系统疾病的早期检测对于提供及时有效的治疗至关重要。我们的研究包括对肺部声音分类方法的全面调查,探索利用人工智能识别和分类呼吸系统疾病的进展。这项调查深入研究了肺音分类模型以及数据增强、特征提取、可解释技术和支持工具,以改进呼吸系统疾病诊断系统。我们的目标是为致力于开发肺部疾病早期检测方法的医疗保健专业人员、研究人员和技术人员提供有意义的见解。本文概述了肺音分类研究的现状,重点介绍了在使用人工智能提高呼吸系统医疗保健诊断方法的准确性和效率方面所取得的进展和面临的挑战。
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