A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): Initial benchmark results and comprehensive analysis

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dc.contributor.authorSagiroğlu, Şeref
dc.contributor.authorTerzi, Ramazan
dc.contributor.authorÇeltikci, Emrah
dc.contributor.authorBörcek, Alp Özgün
dc.contributor.authorAtay, Yılmaz
dc.contributor.authorArslan, Bilgehan
dc.contributor.authorŞahin, Mustafa Çağlar
dc.contributor.authorNernekli, Kerem
dc.contributor.authorDemirezen, Umut
dc.contributor.authorÖzdemir, Okan Bilge
dc.contributor.authorÖzdem Karaca, Kevser
dc.contributor.authorAzgınoğlu, Nuh
dc.date.accessioned2025-06-23T11:27:40Z
dc.date.available2025-06-23T11:27:40Z
dc.date.issued2025
dc.departmentAÇÜ, Rektörlük, Enformatik Bölümü
dc.description.abstractThis article presents a new benchmark MRI dataset called the Gazi Brains Dataset 2020, containing MRI images of 100 patients, and introduces initial experimental results performed on this dataset in comparison with available brain MRI datasets. Furthermore, the dataset is analyzed using eight different deep learning models for high-grade glioma tumor prediction, classification, and detection tasks. Additionally, this study demonstrates the results of an explainable Artificial Intelligence (XAI) approach applied to the trained models. To demonstrate the utility of the proposed dataset, different deep learning models were applied to the problem, and these models were tested on various data and models applied for various tasks such as region of interest extraction, whole tumor segmentation, prediction, detection, and classification with accuracy, precision, recall, and F1-score. The experimental results indicate that the dataset is highly effective for multiple purposes, and the models reached significant results with successful F1-scores ranging between 93.2% and 96.4%. ROI and whole tumor segmentations were successfully performed and compared with seven algorithms with accuracies of 87.61% and 97.18%. The Grad-CAM model also demonstrated satisfactory accuracy across the tests that were conducted. Moreover, this study explores the application of XAI to the trained models, providing interpretability and insights into the decision-making processes. The findings signify that this dataset holds significant potential for various future research directions, including age estimation, gender detection, causal inference with XAI, and disease-related survival analysis.
dc.identifier.doi10.7717/peerj-cs.2920
dc.identifier.endpage63
dc.identifier.issn23765992
dc.identifier.scopus2-s2.0-105007970888
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11494/5609
dc.identifier.volume11
dc.indekslendigikaynakScopus
dc.institutionauthorÖzdemir, Okan Bilge
dc.language.isoen
dc.publisherPeerJ Inc.
dc.relation.ispartofPeerJ Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAnomaly detection
dc.subjectBenchmark
dc.subjectClassification
dc.subjectDeep learning
dc.subjectGazi Brains 2020
dc.subjectModelling
dc.subjectMRI
dc.subjectPrediction
dc.subjectSegmentation
dc.subjectTurkish brain project
dc.titleA novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): Initial benchmark results and comprehensive analysis
dc.typeArticle

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