使用卷积神经网络和变分自编码器检测不平衡数据中的呼吸系统病理
Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data
AI总结：
针对呼吸音数据类别不平衡问题，提出使用变分卷积自编码器生成数据并结合CNN分类，在ICBHI基准上实现三分类F分数0.993和六分类0.990。
摘要：
本文旨在通过呼吸音检测病理状况。使用了ICBHI（国际生物医学与健康信息学会议）基准数据集。该数据集包含920个声音样本，其中810个来自慢性疾病，75个来自非慢性疾病，仅有35个来自健康个体。由于数据集中超过88%的样本属于同一类别（慢性），在确定数据集类别不平衡后，提出了使用变分卷积自编码器生成新的标记数据，并结合其他已知的过采样技术。完成预处理步骤后，使用卷积神经网络（CNN）将呼吸音分类为健康、慢性和非慢性疾病。此外，我们进行了更具挑战性的分类，试图区分不同类型的病理或健康状态：上呼吸道感染（URTI）、慢性阻塞性肺疾病（COPD）、支气管扩张、肺炎和细支气管炎。在三标签分类中，我们取得了高达0.993的F分数，在更具挑战性的六类分类中取得了0.990的F分数。
英文摘要：
The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
