用深度卷积网络分析肺部声音，自动识别慢阻肺及严重程度
Chronic Obstructive Pulmonary Disease Prediction Using Deep Convolutional Network
- 基于声谱特征的深度卷积网络，融合多种音频特征
- 在ICBHI数据集上达96%准确率，优于现有方法
- 适合临床辅助诊断，助力医生应对患者激增
人工智能与深度学习在临床领域应用日益广泛，尤其在利用医学影像和声音进行疾病早期精准检测方面。由于专业人员有限，亟需自动化工具协助临床应对不断增长的患者负荷。呼吸系统疾病如肺癌和糖尿病仍是全球重大健康问题，需及时诊断干预。听诊肺音结合胸部X光是诊断呼吸系统疾病的成熟方法。本研究提出一种基于深度卷积神经网络（CNN）的呼吸音分析方法，用于慢性阻塞性肺病（COPD）的检测，并可分类疾病严重程度（轻度、中度、重度）。采用Librosa库提取的声学特征，包括梅尔频率倒谱系数（MFCCs）、梅尔频谱图、色度、恒定Q色度和色度CENS。在ICBHI数据库上的评估显示，使用10折交叉验证达到96%准确率，未使用交叉验证时为90%。所提模型优于现有方法，展现出临床部署潜力。
原文摘要 · Abstract (English)
Artificial intelligence and deep learning are increasingly applied in the clinical domain, particularly for early and accurate disease detection using medical imaging and sound. Due to limited trained personnel, there is a growing demand for automated tools to support clinicians in managing rising patient loads. Respiratory diseases such as cancer and diabetes remain major global health concerns requiring timely diagnosis and intervention. Auscultation of lung sounds, combined with chest X-rays, is an established diagnostic method for respiratory illness. This study presents a Deep Convolutional Neural Network (CNN)-based approach for the analysis of respiratory sound data to detect Chronic Obstructive Pulmonary Disease (COPD). Acoustic features extracted with the Librosa library, including Mel-Frequency Cepstral Coefficients (MFCCs), Mel-Spectrogram, Chroma, Chroma (Constant Q), and Chroma CENS, were used in training. The system also classifies disease severity as mild, moderate, or severe. Evaluation on the ICBHI database achieved 96% accuracy using 10-fold cross-validation and 90% accuracy without cross-validation. The proposed network outperforms existing methods, demonstrating potential as a practical tool for clinical deployment.
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