Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset

The world has witnessed the success of artificial intelligence deployment for smart healthcare applications. Various studies have suggested that the prevalence of voice disorders in the general population is greater than 10. An automatic diagnosis for voice disorders via machine learning algorithms...

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Main Authors: Chui, K.T., Lytras, M.D., Vasant, P.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23400 /
Published: MDPI AG 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087832634&doi=10.3390%2fapp10134571&partnerID=40&md5=66cf0fd9663ed6bfac8cd881609267e2
http://eprints.utp.edu.my/23400/
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id utp-eprints.23400
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spelling utp-eprints.234002021-08-19T07:22:41Z Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset Chui, K.T. Lytras, M.D. Vasant, P. The world has witnessed the success of artificial intelligence deployment for smart healthcare applications. Various studies have suggested that the prevalence of voice disorders in the general population is greater than 10. An automatic diagnosis for voice disorders via machine learning algorithms is desired to reduce the cost and time needed for examination by doctors and speech-language pathologists. In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c-means clustering (IFCM) algorithm called CGAN-IFCM is proposed for the multi-class voice disorder detection of three common types of voice disorders. Existing benchmark datasets for voice disorders, the Saarbruecken Voice Database (SVD) and the Voice ICar fEDerico II Database (VOICED), use imbalanced classes. A generative adversarial network offers synthetic data to reduce bias in the detection model. Improved fuzzy c-means clustering considers the relationship between adjacent data points in the fuzzy membership function. To explain the necessity of CGAN and IFCM, a comparison is made between the algorithm with CGAN and that without CGAN. Moreover, the performance is compared between IFCM and traditional fuzzy c-means clustering. Lastly, the proposed CGAN-IFCM outperforms existing models in its true negative rate and true positive rate by 9.9-12.9 and 9.1-44.8, respectively. © 2020 by the authors. MDPI AG 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087832634&doi=10.3390%2fapp10134571&partnerID=40&md5=66cf0fd9663ed6bfac8cd881609267e2 Chui, K.T. and Lytras, M.D. and Vasant, P. (2020) Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset. Applied Sciences (Switzerland), 10 (13). http://eprints.utp.edu.my/23400/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The world has witnessed the success of artificial intelligence deployment for smart healthcare applications. Various studies have suggested that the prevalence of voice disorders in the general population is greater than 10. An automatic diagnosis for voice disorders via machine learning algorithms is desired to reduce the cost and time needed for examination by doctors and speech-language pathologists. In this paper, a conditional generative adversarial network (CGAN) and improved fuzzy c-means clustering (IFCM) algorithm called CGAN-IFCM is proposed for the multi-class voice disorder detection of three common types of voice disorders. Existing benchmark datasets for voice disorders, the Saarbruecken Voice Database (SVD) and the Voice ICar fEDerico II Database (VOICED), use imbalanced classes. A generative adversarial network offers synthetic data to reduce bias in the detection model. Improved fuzzy c-means clustering considers the relationship between adjacent data points in the fuzzy membership function. To explain the necessity of CGAN and IFCM, a comparison is made between the algorithm with CGAN and that without CGAN. Moreover, the performance is compared between IFCM and traditional fuzzy c-means clustering. Lastly, the proposed CGAN-IFCM outperforms existing models in its true negative rate and true positive rate by 9.9-12.9 and 9.1-44.8, respectively. © 2020 by the authors.
format Article
author Chui, K.T.
Lytras, M.D.
Vasant, P.
spellingShingle Chui, K.T.
Lytras, M.D.
Vasant, P.
Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset
author_sort Chui, K.T.
title Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset
title_short Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset
title_full Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset
title_fullStr Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset
title_full_unstemmed Combined generative adversarial network and fuzzy C-means clustering for multi-class voice disorder detection with an imbalanced dataset
title_sort combined generative adversarial network and fuzzy c-means clustering for multi-class voice disorder detection with an imbalanced dataset
publisher MDPI AG
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087832634&doi=10.3390%2fapp10134571&partnerID=40&md5=66cf0fd9663ed6bfac8cd881609267e2
http://eprints.utp.edu.my/23400/
_version_ 1741196669841571840
score 11.62408

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