Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2378
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dc.contributor.authorAshames, M.M.-
dc.contributor.authorCeylan, Murat-
dc.contributor.authorJennane, R.-
dc.date.accessioned2022-05-23T20:07:30Z-
dc.date.available2022-05-23T20:07:30Z-
dc.date.issued2021-
dc.identifier.issn2147-6799-
dc.identifier.urihttps://doi.org/10.18201/IJISAE.2021473646-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/2378-
dc.description.abstractOsteoporosis is a systemic skeletal disease characterized by low bone mass density and deterioration of the micro-architectural structure of the bone tissue, increasing bone fragility, and the probability of fracture. In this study, we propose a non-invasive method for osteoporosis classification using X-ray images (plain radiographs) of the ankle. Convolutional Neural Networks along with Data Augmentation techniques and Deep Transfer Learning Architectures are combined to classify X-ray images of healthy and osteoporotic patients. The proposed approach achieved an accuracy of 99% using ResNet50, and 100% with GoogleNet. © 2021, Ismail Saritas. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherIsmail Saritasen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCNNen_US
dc.subjectData augmentationen_US
dc.subjectOsteoporosisen_US
dc.subjectTransfer learningen_US
dc.subjectX-rayen_US
dc.titleDeep Transfer Learning and Majority Voting Approaches for Osteoporosis Classificationen_US
dc.typeArticleen_US
dc.identifier.doi10.18201/IJISAE.2021473646-
dc.identifier.scopus2-s2.0-85124485434en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.identifier.volume9en_US
dc.identifier.issue4en_US
dc.identifier.startpage256en_US
dc.identifier.endpage265en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57449083600-
dc.authorscopusid56276648900-
dc.authorscopusid55996640900-
dc.identifier.trdizinid508032en_US
dc.identifier.scopusqualityQ4-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
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