Classification of Lung Sounds With CNN Model Using Parallel Pooling Structure
Classification of Lung Sounds With CNN Model Using Parallel Pooling Structure
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DOI：
10.1109/access.2020.3000111
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Sengur, Abdulkadir
中科院分区：
文献类型：
--
作者：
Demir, Fatih;Ismael, Aras Masood;Sengur, Abdulkadir
电子听诊器记录的各种肺音的识别在呼吸系统疾病的早期诊断中起着重要的作用。为了提高专家评估的准确性，机器学习技术在过去30年中得到了广泛应用。在当前的研究中，提出了一种新的预训练卷积神经网络（CNN）模型来提取深度特征。在CNN架构中，平均池化层和最大池化层并行连接，以提高分类性能。使用随机子空间集成（RSE）方法将深度特征用作线性判别分析（LDA）分类器的输入。针对称为ICBHI 2017的挑战数据集对所提出的方法进行了评估。与使用相同数据集的其他现有方法相比，深度特征和具有RSE方法的LDA提供了最好的准确度评分，将分类准确度提高了5.75%。
The recognition of various lung sounds recorded using electronic stethoscopes plays a significant role in the early diagnoses of respiratory diseases. To increase the accuracy of specialist evaluations, machine learning techniques have been intensely employed during the past 30 years. In the current study, a new pretrained Convolutional Neural Network (CNN) model is proposed for the extraction of deep features. In the CNN architecture, an average-pooling layer and a max-pooling layer are connected in parallel in order to boost classification performance. The deep features are utilized as the input of the Linear Discriminant Analysis (LDA) classifier using the Random Subspace Ensembles (RSE) method. The proposed method was evaluated against a challenge dataset known as ICBHI 2017. The deep features and the LDA with RSE method provided the best accuracy score when compared to other existing methods using the same dataset, improving the classification accuracy by 5.75%.