论文标题
使用基于EMD-CWT的混合缩放图检测肺听觉呼吸道疾病的轻巧CNN模型
A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram
论文作者
论文摘要
通过听诊来聆听肺部声音对于检查异常的呼吸系统至关重要。在缺乏熟练的医生的低资源环境中，对肺听觉声音的自动分析可能对卫生系统有益。在这项工作中，我们提出了一种轻巧的卷积神经网络（CNN）体系结构，以使用基于肺部声音的混合尺度图的特征对呼吸道疾病进行分类。混合缩放图特征利用经验模式分解（EMD）和连续小波变换（CWT）。使用公开可用的2017年肺部声音数据集中的独立火车验证套件来研究拟议的方案的性能。使用提出的框架，三元慢性分类的加权精度分数为99.20％，六级病理分类的加权精度得分为99.05％，在准确性方面的表现分别优于众所周知的VGG16，其精度分别为0.52％和1.77％。提出的CNN模型还表现出其他当代轻量级模型，同时在计算上可比性。
Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the health systems in low-resource settings where there is a lack of skilled physicians. In this work, we propose a lightweight convolutional neural network (CNN) architecture to classify respiratory diseases using hybrid scalogram-based features of lung sounds. The hybrid scalogram features utilize the empirical mode decomposition (EMD) and continuous wavelet transform (CWT). The proposed scheme's performance is studied using a patient independent train-validation set from the publicly available ICBHI 2017 lung sound dataset. Employing the proposed framework, weighted accuracy scores of 99.20% for ternary chronic classification and 99.05% for six-class pathological classification are achieved, which outperform well-known and much larger VGG16 in terms of accuracy by 0.52% and 1.77% respectively. The proposed CNN model also outperforms other contemporary lightweight models while being computationally comparable.
