Publication:
Hybrid voice spectrogram-chromogram based deep learning (HVSC-DL) model for the detection of Parkinson's disease

dc.contributor.coauthorHanci N.B., Kurt I., Ulukaya S., Erdem O., Guler S.,
dc.contributor.departmentSchool of Medicine
dc.contributor.kuauthorUzun, Cem
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2025-03-06T21:00:31Z
dc.date.issued2024
dc.description.abstractParkinson's disease is one of the serious neurological disorders that restricts the life quality of individuals significantly. The changes in sound signals contain important clues for detecting the disease at an early stage. In this study, a newly collected Parkinson's voice dataset is introduced, and preliminary results with classical machine learning and the proposed three alternative deep learning models are presented comparatively. We observed that our proposed two-channel Hybrid Voice Spectrogram-Chromogram based Deep Learning Model (HVSC-DL) with the patient-healthy classification accuracy rates of 96.6%, 92.9% and 94.5% on /a/, /o/ and /i/ sounds respectively, showed superior performance compared to pure tone chromogram and spectrogram based models.
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.identifier.doi10.23919/SPA61993.2024.10715598
dc.identifier.eissn2326-0262
dc.identifier.isbn9788362065486
dc.identifier.issn2326-0262
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85207914628
dc.identifier.urihttps://doi.org/10.23919/SPA61993.2024.10715598
dc.identifier.urihttps://hdl.handle.net/20.500.14288/27909
dc.keywordsAudio processing
dc.keywordsDeep neural networks
dc.keywordsNeurological disorder
dc.keywordsParkinson disease
dc.keywordsVoice analysis
dc.language.isoeng
dc.publisherIEEE Computer Society
dc.relation.ispartofSignal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA
dc.subjectMedicine
dc.titleHybrid voice spectrogram-chromogram based deep learning (HVSC-DL) model for the detection of Parkinson's disease
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorUzun, Cem
local.publication.orgunit1SCHOOL OF MEDICINE
local.publication.orgunit2School of Medicine
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