SimCLR-based Self-Supervised Learning Approach for Limited Brain MRI and Unlabeled Images

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dc.contributor.authorTalu, Muhammed
dc.contributor.authorFırıldak, Kazım
dc.contributor.authorÇelik, Gaffari
dc.date.accessioned2026-04-04T13:15:07Z
dc.date.available2026-04-04T13:15:07Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description.abstractIn this study, a SimCLR-based model is proposed for the classification of unlabeled brain tumor images in medical imaging using a self-supervised learning (SSL) technique. Additionally, the performances of different SSL techniques (Barlow Twins, NnCLR, and SimCLR) are analyzed to evaluate the performance of the proposed model. Three different datasets, consisting of pituitary, meningioma, and glioma brain tumors as well as non-tumor images, were used as the dataset. Out of a total of 7,671 images, 6,128 were used as unlabeled data, and the model was trained with both labeled and unlabeled data. The proposed model achieved high performance with unlabeled data, reducing the need for manual labeling. As a result, the model demonstrated superior performance compared to other models, with high performance values such as 99.35% c_acc and 96.31% p_acc.
dc.identifier.doi10.17798/bitlisfen.1558069
dc.identifier.endpage1313
dc.identifier.issn2147-3129
dc.identifier.issn2147-3188
dc.identifier.issue4
dc.identifier.startpage1304
dc.identifier.trdizinid1294246
dc.identifier.urihttps://doi.org/10.17798/bitlisfen.1558069
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1294246
dc.identifier.urihttps://hdl.handle.net/11616/107801
dc.identifier.volume13
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofBitlis Eren Üniversitesi Fen Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20250329
dc.subjectTıbbi İnformatik
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectRadyoloji
dc.subjectNükleer Tıp
dc.subjectTıbbi Görüntüleme
dc.titleSimCLR-based Self-Supervised Learning Approach for Limited Brain MRI and Unlabeled Images
dc.typeArticle

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