Development of classification methods for wheeze and crackle using mel frequency cepstral coefficient (MFCC): a deep learning approach

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

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Abstract

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.

Item Type:	Article (Journal)
Additional Information:	4964/86321
Uncontrolled Keywords:	Deep Learning, Convolutional Neural Network, Mel Frequency Cepstral Coefficient, Respiratory Sounds, Adventitious Sounds, Sustainable Development Goals
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr. Raini Hassan
Date Deposited:	16 Dec 2020 15:34
Last Modified:	16 Dec 2020 15:34
URI:	http://irep.iium.edu.my/id/eprint/86321

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