Convolutional Neural Network for Breathing Phase Detection in Lung Sounds

Jácome, Cristina; Ravn, Johan; Holsbø, Einar; Aviles-Solis, Juan Carlos; Melbye, Hasse; Bongo, Lars Ailo

doi:10.3390/s19081798

Cited by 43 publications

(28 citation statements)

References 20 publications

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“…In this method is used to identify the blood vessels in the entire region of image for image segmentation. This method used a fully connected convolutional neural network for classification of image segmentation of cardiovascular disease within the lung nodules [53,54] (Fig. 9).…”

Section: Automatic Edge Detection Using Deep Learningmentioning

confidence: 99%

“…Thus, most of the companies will use the data compression technique in order to run out of space. Machine Learning algorithms like support vector machine combines with the DCT discrete cosine transform achieve the level as compared to the RBF and multilayer preceptor [54,57]. DCT is based on the JPEG compression algorithm one of the most widely used image compression algorithms.…”

Section: Image Compressionmentioning

confidence: 99%

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Solving Image Processing Critical Problems Using Machine Learning

Sharma

Gupta

Jaiswal

2021

Studies in Big Data

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The history of vision can go back around 543 BC. From fossil studies, researchers found out that no. of animal species floats out and soon develops the first animal with eyes. As evolution in bigger species around fifty percent of neurons in our body are involved in the visual processing. Vision allows human to perceive, comprehend, and understand surrounding them while computer vision aims to duplicate the impact of human vision electronically in the form of images. The history of making artificial vison starts around 1545, Gemma Frisius, and Encylopedie in eighteenth century. The foundation period of the concept lies from 1943 to 1956, during this period research is cantered towards understanding the concept of machine learning intelligence, artificial neural system, and automata theory computation. The first work in the field of Artificial intelligence in computer vision and image processing was done in MIT called "Summer Vision Project". The rise of artificial intelligence starts and it is used widely in every field of technology. As the technology advances machine learning is used in almost every field. This book chapter provides you some details how the machine learning is used in image processing and to solve the current problem and challenge in computer vison like: Denoising, Image compression, Image color enhancement, segmentation, etc. Machine learning algorithm like Neural Network, Support Vector Machine, Genetic Algorithm, Convolutional Neural Network, etc. were discussed in the book chapter.

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Section: Automatic Edge Detection Using Deep Learningmentioning

confidence: 99%

Section: Image Compressionmentioning

confidence: 99%

Solving Image Processing Critical Problems Using Machine Learning

Sharma

Gupta

Jaiswal

2021

Studies in Big Data

View full text Add to dashboard Cite

show abstract

“…In Reference [12], Jacome et al also proposed a CNN to deal with respiratory sounds for detecting breathing phase with a 97% of success in inspiration detection and a 87% in expiration.…”

Section: Respiratory Sounds Detectionmentioning

confidence: 99%

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

García-Ordás

Benítez-Andrades

García-Rodríguez

et al. 2020

Sensors

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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.

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“…Literatürde var olan ve üç alt kümesi bulunan veri seti kullanılarak değerlendirilmiştir ve ortalama olarak %84 başarım elde edilmiştir. [18]. Demir ve ark.…”

Section: İlgi̇li̇ çAlişmalar (Related Work)unclassified

Akciğer Seslerinin Derin Öğrenme İle Sınıflandırılması

2020

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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In this study, a method based on deep learning has been proposed for the classification of lung sounds. For this purpose, the Convolutional Neural Network (CNN) has been designed. In addition, experiments are carried out using different machine learning methods based on feature extraction. Figure A. Schema of the Proposed MethodPurpose: The focuses is on automatic diagnosis of lung diseases, one of the most important issues in public health. There have been many studies on this subject in the literature, but most of these studies consist of traditional methods. The aim of this study is to increase the classification performance of lung sounds with deep learning. Theory and Methods:The proposed method for the classification of lung sounds consists of five steps. First, lung sound signals are pre-processed, and spectrograms are obtained. After applying the data augment process to spectrograms, spectrogram images are given as input to the designed ESA model and the classification process is made.Results: Experiments to evaluate the effectiveness of different methods are carried out using the ICBHI 2017 data set consisting of four classes commonly used in the literature. On average, 64.5% accuracy was obtained from the proposed method. The simulation and experimental results are presented and compared in Section 6. Conclusion:In this study, deep learning is discussed to improve the classification performance of lung sounds. By designing a 12-layer CNN, spectrogram images are given to the first layer, and the classification process is made. Before the data augment, an average of 60.14% classification performance is obtained. After the data augmentation, an average classification performance of 64.50% is obtained. In addition, in order to compare the performance of the proposed method with other machine learning methods, MFCC features are extracted from the data set samples and the classification process is made with SVM and K-NN.

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Convolutional Neural Network for Breathing Phase Detection in Lung Sounds

Cited by 43 publications

References 20 publications

Solving Image Processing Critical Problems Using Machine Learning

Solving Image Processing Critical Problems Using Machine Learning

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

Akciğer Seslerinin Derin Öğrenme İle Sınıflandırılması

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