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Open AccessArticle

Combined Generative Adversarial Network and Fuzzy C-Means Clustering for Multi-Class Voice Disorder Detection with an Imbalanced Dataset

1
School of Science and Technology, The Open University of Hong Kong, Hong Kong, China
2
King Abdulaziz University, Jeddah, P.O. Box 34689, Saudi Arabia
3
Effat College of Engineering, Effat University, Jeddah, P.O. Box 34689, Saudi Arabia
4
Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, Malaysia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(13), 4571; https://doi.org/10.3390/app10134571
Received: 2 June 2020 / Revised: 29 June 2020 / Accepted: 29 June 2020 / Published: 1 July 2020
(This article belongs to the Special Issue Signal Processing and Machine Learning for Biomedical Data)
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. View Full-Text
Keywords: artificial intelligence; fuzzy c-means clustering; generative adversarial network; imbalanced dataset; machine learning; multi-class detection; smart healthcare; synthetic data; voice disorders artificial intelligence; fuzzy c-means clustering; generative adversarial network; imbalanced dataset; machine learning; multi-class detection; smart healthcare; synthetic data; voice disorders
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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. Appl. Sci. 2020, 10, 4571.

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