Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6379
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Daglı, Kaya | - |
dc.contributor.author | Eroğul, Osman | - |
dc.date.accessioned | 2021-09-11T15:36:08Z | - |
dc.date.available | 2021-09-11T15:36:08Z | - |
dc.date.issued | 2020 | en_US |
dc.identifier.citation | Medical Technologies National Congress (TIPTEKNO) -- NOV 19-20, 2020 -- ELECTR NETWORK | en_US |
dc.identifier.isbn | 978-1-7281-8073-1 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6379 | - |
dc.description.abstract | Brain tumors threathen human health significantly. Misdiagnosis of these tumors decrease effectiveness of decisions for intervention and patient's state of health. The conventional method to differentiate brain tumors is by the inspection of magnetic resonance images by clinicians. Since there are various types of brain tumors and there are many images that clinicians should examine, this method is both prone to human errors and causes excessive time consumption. In this study, the most common brain tumor types; Glioma, Meningioma and Pituitary are classified using deep learning models. While the main objective of this study is to have a high rate of accuracy, the time spent is also examined. The aim of this study is to ease clinicians work load and have a time efficient classification system. The system which has been built has an accuracy up to 90 %. | en_US |
dc.description.sponsorship | Biyomedikal ve Klinik Muhendisligi Dernegi, Izmir Ekonomi Univ, Izmir Katip Celebi Univ | en_US |
dc.language.iso | tr | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2020 Medical Technologies Congress (Tiptekno) | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Accuracy | en_US |
dc.subject | Brain Tumor | en_US |
dc.subject | Classification | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Magnetic Resonance Images | en_US |
dc.subject | Time Consumption | en_US |
dc.title | Classification of Brain Tumors Via Deep Learning Models | en_US |
dc.type | Conference Object | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Biomedical Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü | tr_TR |
dc.identifier.wos | WOS:000659419900018 | en_US |
dc.identifier.scopus | 2-s2.0-85099462200 | en_US |
dc.institutionauthor | Eroğul, Osman | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | Medical Technologies National Congress (TIPTEKNO) | en_US |
item.openairetype | Conference Object | - |
item.languageiso639-1 | tr | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.2. Department of Biomedical Engineering | - |
Appears in Collections: | Biyomedikal Mühendisliği Bölümü / Department of Biomedical Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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