Convolutional neural networks based efficient approach for classification of lung diseases

Abstract

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.

Classification of Lung Sounds With CNN Model Using Parallel Pooling Structure

“…In [25], the wavelet decomposition and STFT were combined as a feature set, producing a best accuracy level of 57.88% using the SVM classifier. In [26], two methods were proposed for lung sound classification. First, lung sounds were classified with the transfer learning technique, trained by applying fine-tuning to the pretrained VGG16 model, and achieved a best accuracy level of 63.09%.…”

An Automated System for Classification of Chronic Obstructive Pulmonary Disease and Pneumonia Patients Using Lung Sound Analysis

“…The proposed technique has provided an efficient approach with outstanding classification accuracy. It has outperformed as compared to existing techniques on other multiple pulmonic pathologies from LS analysis due to its simple statistical features, low computation, and accuracy [ 6 , 7 , 12 , 20 , 22 , 24 , 25 , 28 , 29 ]. The performance analysis of the proposed diagnostic technique with existing pulmonic pathologies methods is shown in Table 9 .…”

“…Recently, a research article is published in which a convolutional neural network (CNN) is implemented on the LS database of the international conference on biomedical and health informatics (ICBHI). But the research work only focused to classify the adventitious sounds found in various pulmonic illnesses [ 28 ]. In another research, a novel approach called variational convolutional autoencoder is presented for unbalanced data and implemented on the same database [ 29 ].…”

Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?

“…The spectrogram obtained using the short-time Fourier transform (STFT) is one of the most used tools in audio analysis and processing, since it describes the evolution of the frequency components over time. The STFT representation (F) of a given discrete signal is given by [ 35 ]: where is a window function centered at instant n .…”

“…The most common features and machine learning algorithms employed in the literature to detect or classify ARS have been reported [ 6 ], including spectral features [ 25 ], mel-frequency cepstral coefficients (MFCCs) [ 26 ], entropy [ 27 ], wavelet coefficients [ 28 ], rule-based models [ 29 ], logistic regression models [ 30 ], support vector machines (SVM) [ 31 ], and artificial neural networks [ 32 ]. More recently, deep learning strategies have also been introduced, where the feature extraction and classification steps are merged into the learning algorithm [ 33 , 34 , 35 ].…”