Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach
The most common method used by physicians and pulmonologists to evaluate the state of the lung is by listening to the acoustics of the patient's breathing by a stethoscope. Misdiagnosis and eventually, mistreatment are rampant if auscultation is not done properly. There have been efforts to add...
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my.iium.irep.863212020-12-16T07:34:25Z http://irep.iium.edu.my/86321/ Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach Mohamed Sadi, Tinir Hassan, Raini QA75 Electronic computers. Computer science T Technology (General) The most common method used by physicians and pulmonologists to evaluate the state of the lung is by listening to the acoustics of the patient's breathing by a stethoscope. Misdiagnosis and eventually, mistreatment are rampant if auscultation is not done properly. There have been efforts to address this problem using a myriad of Machine Learning algorithms, but little has been done using Deep Learning. A Convolutional Neural Network (CNN) model with Mel Frequency Cepstral Coefficient (MFCC) is expected to mitigate these problems. The problem has been in the paucity of large enough datasets. Results show 0.76 and 0.60 for recall for wheeze and crackle respectively and these number are set to increase with optimization and larger, more diverse datasets. 2020-12-14 Article PeerReviewed application/pdf en http://irep.iium.edu.my/86321/1/166-Article%20Text-1004-1-10-20201214.pdf Mohamed Sadi, Tinir and Hassan, Raini (2020) Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach. International Journal on Perceptive and Cognitive Computing (IJPCC), 6 (2). pp. 107-114. E-ISSN 2462-229X https://journals.iium.edu.my/kict/index.php/IJPCC https://doi.org/10.31436/ijpcc.v6i2.166 |
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QA75 Electronic computers. Computer science T Technology (General) Mohamed Sadi, Tinir Hassan, Raini Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach |
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The most common method used by physicians and pulmonologists to evaluate the state of the lung is by listening to the acoustics of the patient's breathing by a stethoscope. Misdiagnosis and eventually, mistreatment are rampant if auscultation is not done properly. There have been efforts to address this problem using a myriad of Machine Learning algorithms, but little has been done using Deep Learning. A Convolutional Neural Network (CNN) model with Mel Frequency Cepstral Coefficient (MFCC) is expected to mitigate these problems. The problem has been in the paucity of large enough datasets. Results show 0.76 and 0.60 for recall for wheeze and crackle respectively and these number are set to increase with optimization and larger, more diverse datasets. |
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Article |
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Mohamed Sadi, Tinir Hassan, Raini |
author_facet |
Mohamed Sadi, Tinir Hassan, Raini |
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Mohamed Sadi, Tinir |
title |
Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach |
title_short |
Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach |
title_full |
Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach |
title_fullStr |
Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach |
title_full_unstemmed |
Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach |
title_sort |
development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (mfcc): a deep learning approach |
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2020 |
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http://irep.iium.edu.my/86321/1/166-Article%20Text-1004-1-10-20201214.pdf http://irep.iium.edu.my/86321/ https://journals.iium.edu.my/kict/index.php/IJPCC https://doi.org/10.31436/ijpcc.v6i2.166 |
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1687393149021847552 |
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13.160551 |