Voice pathology detection and classification using convolutional neural network model

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dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorAbdulkareem, Karrar Hameed
dc.contributor.authorMostafa, Salama A.
dc.contributor.authorGhani, Mohd Khanapi Abd
dc.contributor.authorMaashi, Mashael S.
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorOleagordia Ruiz, Ibon
dc.contributor.authorAlhakami, Hosam
dc.contributor.authorAl-Dhief, Fahad Taha
dc.date.accessioned2026-03-18T11:06:31Z
dc.date.available2026-03-18T11:06:31Z
dc.date.issued2020-05-27
dc.date.updated2026-03-18T11:06:31Z
dc.description.abstractVoice 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 Saarbrucken 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.en
dc.description.sponsorshipThis research received funding from Basque Country Governmenten
dc.identifier.citationMohammed, M. A., Abdulkareem, K. H., Mostafa, S. A., Ghani, M. K. A., Maashi, M. S., Garcia-Zapirain, B., Oleagordia, I., Alhakami, H., & Al-Dhief, F. T. (2020). Voice pathology detection and classification using convolutional neural network model. Applied Sciences (Switzerland), 10(11). https://doi.org/10.3390/APP10113723
dc.identifier.doi10.3390/APP10113723
dc.identifier.eissn2076-3417
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5506
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2020 by the authors
dc.subject.otherConvolutional neural network
dc.subject.otherResidual network (ResNet34)
dc.subject.otherSaarbrücken voice database
dc.subject.otherThe vowel /a/
dc.subject.otherVoice pathology classification
dc.subject.otherVoice pathology detection
dc.titleVoice pathology detection and classification using convolutional neural network modelen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue11
oaire.citation.titleApplied Sciences (Switzerland)
oaire.citation.volume10
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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