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Applied Sciences
Volume 10
Issue 11
10.3390/app10113723
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Article

Voice Pathology Detection and Classification Using Convolutional Neural Network Model

by
Mazin Abed Mohammed
1,*,
Karrar Hameed Abdulkareem
2,
Salama A. Mostafa
3,
Mohd Khanapi Abd Ghani
4,
Mashael S. Maashi
5,
Begonya Garcia-Zapirain
6,
Ibon Oleagordia
6,
Hosam Alhakami
7 and
Fahad Taha AL-Dhief
8
1
College of Computer Science and Information Technology, University of Anbar, 11, Ramadi 31001, Anbar, Iraq
2
College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
3
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 86400, Malaysia
4
Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
5
Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
6
eVIDA Lab., University of Deusto, Avda/Universidades 24, 48007 Bilbao, Spain
7
Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21421, Saudi Arabia
8
Faculty of Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(11), 3723; https://doi.org/10.3390/app10113723
Received: 30 April 2020 / Revised: 25 May 2020 / Accepted: 26 May 2020 / Published: 27 May 2020
(This article belongs to the Special Issue Medical Artificial Intelligence)
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Abstract

Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy. View Full-Text
Keywords: voice pathology detection; voice pathology classification; convolutional neural network; Saarbrücken voice database; the vowel /a/; residual network (ResNet34) voice pathology detection; voice pathology classification; convolutional neural network; Saarbrücken voice database; the vowel /a/; residual network (ResNet34)
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MDPI and ACS Style

Mohammed, M.A.; Abdulkareem, K.H.; Mostafa, S.A.; Khanapi Abd Ghani, M.; Maashi, M.S.; Garcia-Zapirain, B.; Oleagordia, I.; Alhakami, H.; AL-Dhief, F.T. Voice Pathology Detection and Classification Using Convolutional Neural Network Model. Appl. Sci. 2020, 10, 3723. https://doi.org/10.3390/app10113723

AMA Style

Mohammed MA, Abdulkareem KH, Mostafa SA, Khanapi Abd Ghani M, Maashi MS, Garcia-Zapirain B, Oleagordia I, Alhakami H, AL-Dhief FT. Voice Pathology Detection and Classification Using Convolutional Neural Network Model. Applied Sciences. 2020; 10(11):3723. https://doi.org/10.3390/app10113723

Chicago/Turabian Style

Mohammed, Mazin A., Karrar H. Abdulkareem, Salama A. Mostafa, Mohd Khanapi Abd Ghani, Mashael S. Maashi, Begonya Garcia-Zapirain, Ibon Oleagordia, Hosam Alhakami, and Fahad T. AL-Dhief. 2020. "Voice Pathology Detection and Classification Using Convolutional Neural Network Model" Applied Sciences 10, no. 11: 3723. https://doi.org/10.3390/app10113723

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