In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

中文翻译：

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基于深度学习的呼吸音分析检测慢性阻塞性肺疾病


近年来，机器学习和深度学习等技术在为医疗领域的挑战提供辅助解决方案方面发挥了至关重要的作用。它们还利用医学成像和音频分析来提高早期和及时​​疾病检测的预测准确性。由于缺乏训练有素的人力资源，医生们欢迎这种技术援助，因为这可以帮助他们应对更多的患者。除了癌症、糖尿病等重大健康疾病外，呼吸系统疾病的影响也逐渐上升，并威胁到社会的生命。早期诊断和立即治疗对于呼吸系统疾病至关重要，因此呼吸音和胸部 X 光检查非常有益。所提出的研究工作旨在应用基于卷积神经网络的深度学习方法来协助医学专家，对用于慢性阻塞性肺检测的医学呼吸音频数据进行详细而严格的分析。在进行的实验中，我们使用了 Librosa 机器学习库功能，例如 MFCC、Mel-Spectrogram、Chroma、Chroma (Constant-Q) 和 Chroma CENS。所提出的系统还可以解释所识别疾病的严重程度，例如轻度、中度或急性。调查结果验证了所提出的深度学习方法的成功。系统分类准确率已提升至 ICBHI 分数 93%。此外，在进行的实验中，我们应用了十次分割的 K 折交叉验证来优化所提出的深度学习方法的性能。