Classification of stroke using machine learning techniques : review study

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dc.authoridRadwan Qasrawi / 0000-0001-8671-7026en_US
dc.authorscopusidRadwan Qasrawi / 57212263325en_US
dc.authorwosidRadwan Qasrawi / AAA-6245-2019en_US
dc.contributor.authorSawan, Aktham
dc.contributor.authorAwad, Mohammed
dc.contributor.authorQasrawi, Radwan
dc.date.accessioned2023-10-22T12:20:35Z
dc.date.available2023-10-22T12:20:35Z
dc.date.issued2023en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractAbstract—Presently, stroke is the leading cause of adult injury worldwide. The World Health Organization estimates that each year 15 million people around the world suffer a stroke. Five million of them die, and another five million are disabled for life. There is a chance to dramatically enhance the classification of strokes in the early stages. In this article, we reviewed all portable devices that produced electroencephalogram(EEG) data and all machine learning (ML) methods and deep-learning methods used to identify stroke using EEG data, and we noted that the amount of work on ML and deep learning in analyzing EEG data have increased rapidly in recent years. Such analysis has achieved greater precision compared to that conventional methods. We also discussed in this study the opportunities and key challenges for improving the accuracy of future work.en_US
dc.identifier.citationSawan, A., Awad, M., & Qasrawi, R. (2023, May). Classification of Stroke Using Machine Learning Techniques: Review Study. In 2023 International Conference on Control, Automation and Diagnosis (ICCAD) (pp. 1-8). IEEE.en_US
dc.identifier.doi10.1109/ICCAD57653.2023.10152317en_US
dc.identifier.isbn9798350347074
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85164130240en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ICCAD57653.2023.10152317
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3987
dc.identifier.volume33en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorQasrawi, Radwan
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofIEEEen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learning , Imaging , Organizations , Stroke (medical condition) , Brain modeling , Electroencephalography , Real-time systemsen_US
dc.subjectDeep Learningen_US
dc.subjectImagingen_US
dc.subjectOrganizationsen_US
dc.subjectStroke (Medical Condition)en_US
dc.subjectBrain Modelingen_US
dc.subjectElectroencephalographyen_US
dc.subjectReal-Time Systemsen_US
dc.titleClassification of stroke using machine learning techniques : review studyen_US
dc.typeConference Objecten_US

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