Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/9973
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dc.contributor.authorTanveer, M.en_US
dc.contributor.authorRashid, Ashraf Haroonen_US
dc.date.accessioned2022-05-05T15:56:07Z-
dc.date.available2022-05-05T15:56:07Z-
dc.date.issued2022-
dc.identifier.citationTanveer, M., Rashid, A. H., Kumar, R., & Balasubramanian, R. (2022). Parkinson's disease diagnosis using neural networks: Survey and comprehensive evaluation. Information Processing and Management, 59(3) doi:10.1016/j.ipm.2022.102909en_US
dc.identifier.issn0306-4573-
dc.identifier.otherEID(2-s2.0-85126616646)-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/9973-
dc.identifier.urihttps://doi.org/10.1016/j.ipm.2022.102909-
dc.description.abstractParkinson's disease (PD) is a chronic neurodegenerative disease of that predominantly affects the elderly in today's world. For the diagnosis of the early stages of PD, effective and powerful automated techniques are needed by recent enabling technologies as a tool. In this study, we present a comprehensive review of papers from 2013 to 2021 on the diagnosis of PD and its subtypes using artificial neural networks (ANNs) and deep neural networks (DNNs). We present detailed information and analysis regarding the usage of various modalities, datasets, architectures and experimental configurations in a succinct manner. We also present an in-depth comparative analysis of various proposed architectures. Finally, we present a number of relevant future directions for researchers in this area. © 2022 Elsevier Ltden_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.sourceInformation Processing and Managementen_US
dc.subjectDeep neural networks|Network architecture|Neurodegenerative diseases|Automated techniques|Comparative analyzes|Comprehensive evaluation|Deep learning|Disease diagnosis|Enabling technologies|Machine-learning|Multi-modal learning|Neural-networks|Parkinson's disease|Diagnosisen_US
dc.titleParkinson's disease diagnosis using neural networks: Survey and comprehensive evaluationen_US
dc.typeJournal Articleen_US
Appears in Collections:Department of Mathematics

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