Convolutional neural networks based efficient approach for classification of lung diseases
Convolutional neural networks based efficient approach for classification of lung diseases
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DOI：
10.1007/s13755-019-0091-3
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发表时间：
2019-12-23
影响因子：
6
通讯作者：
Bajaj, Varun
中科院分区：
文献类型：
--
作者：
Demir, Fatih;Sengur, Abdulkadir;Bajaj, Varun
肺部疾病是世界上第三大常见死亡原因，其治疗在医学领域具有重要意义。为了利用人工智能兼容设备诊断肺部疾病，并帮助专家进行诊断，文献中已经进行了许多使用听诊器记录的肺音的研究。本文使用ICBHI 2017数据库对肺音进行分类，该数据库包含不同的样本频率、噪声和背景声。首先利用时频方法将肺音信号转换为频谱图图像。将短时傅立叶变换(STFT)方法视为时频变换。采用两种基于深度学习的方法对肺音进行分类。在第一种方法中，使用预先训练好的深度卷积神经网络(CNN)模型进行特征提取，使用支持向量机(SVM)分类器对肺音进行分类。在第二种方法中，通过用于肺音分类的谱图图像对预先训练的深度CNN模型进行微调(转移学习)。采用十倍交叉验证的方法对所提方法的准确性进行了检验。第一种方法的准确率为65.5%，第二种方法的准确率为63.09%。然后将获得的精度与现有的一些结果进行比较，可以看出所获得的分数比其他结果更好。
Treatment of lung diseases, which are the third most common cause of death in the world, is of great importance in the medical field. Many studies using lung sounds recorded with stethoscope have been conducted in the literature in order to diagnose the lung diseases with artificial intelligence-compatible devices and to assist the experts in their diagnosis. In this paper, ICBHI 2017 database which includes different sample frequencies, noise and background sounds was used for the classification of lung sounds. The lung sound signals were initially converted to spectrogram images by using time-frequency method. The short time Fourier transform (STFT) method was considered as time-frequency transformation. Two deep learning based approaches were used for lung sound classification. In the first approach, a pre-trained deep convolutional neural networks (CNN) model was used for feature extraction and a support vector machine (SVM) classifier was used in classification of the lung sounds. In the second approach, the pre-trained deep CNN model was fine-tuned (transfer learning) via spectrogram images for lung sound classification. The accuracies of the proposed methods were tested by using the ten-fold cross validation. The accuracies for the first and second proposed methods were 65.5% and 63.09%, respectively. The obtained accuracies were then compared with some of the existing results and it was seen that obtained scores were better than the other results.