Publication: Hybrid voice spectrogram-chromogram based deep learning (HVSC-DL) model for the detection of Parkinson's disease
dc.contributor.coauthor | Hanci N.B., Kurt I., Ulukaya S., Erdem O., Guler S., | |
dc.contributor.department | School of Medicine | |
dc.contributor.kuauthor | Uzun, Cem | |
dc.contributor.schoolcollegeinstitute | SCHOOL OF MEDICINE | |
dc.date.accessioned | 2025-03-06T21:00:31Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Parkinson'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.indexedby | Scopus | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.identifier.doi | 10.23919/SPA61993.2024.10715598 | |
dc.identifier.eissn | 2326-0262 | |
dc.identifier.isbn | 9788362065486 | |
dc.identifier.issn | 2326-0262 | |
dc.identifier.quartile | N/A | |
dc.identifier.scopus | 2-s2.0-85207914628 | |
dc.identifier.uri | https://doi.org/10.23919/SPA61993.2024.10715598 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/27909 | |
dc.keywords | Audio processing | |
dc.keywords | Deep neural networks | |
dc.keywords | Neurological disorder | |
dc.keywords | Parkinson disease | |
dc.keywords | Voice analysis | |
dc.language.iso | eng | |
dc.publisher | IEEE Computer Society | |
dc.relation.ispartof | Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA | |
dc.subject | Medicine | |
dc.title | Hybrid voice spectrogram-chromogram based deep learning (HVSC-DL) model for the detection of Parkinson's disease | |
dc.type | Conference Proceeding | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Uzun, Cem | |
local.publication.orgunit1 | SCHOOL OF MEDICINE | |
local.publication.orgunit2 | School of Medicine | |
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