Braindetective: An advanced deep learning application for early detection, segmentation and classification of brain tumours using MRI images

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dc.authorid0000-0001-9840-4211
dc.authorid0009-0001-0667-3906
dc.authorid0009-0002-6202-8263
dc.authorid0009-0009-4492-9405
dc.authorid0009-0009-1167-2463
dc.authorid0009-0001-3852-0525
dc.authorid0000-0002-2126-8757
dc.contributor.authorTokatlı, Nazlı
dc.contributor.authorBayram, Mücahit
dc.contributor.authorOgur, Hatice
dc.contributor.authorKılıç, Yusuf
dc.contributor.authorHan, Vesile
dc.contributor.authorBatur, Kutay Can
dc.contributor.authorAltun, Halis
dc.date.accessioned2025-11-19T13:20:12Z
dc.date.available2025-11-19T13:20:12Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü
dc.description.abstractThis study aims to create deep learning models for the early identification and classification of brain tumours. Models like U-Net, DAU-Net, DAU-Net 3D, and SGANet have been used to evaluate brain MRI images accurately. Magnetic resonance imaging (MRI) is the most commonly used method in brain tumour diag nosis, but it is a complicated procedure due to the brain’s complex structure. This study looked into the ability of deep learning architectures to increase the accuracy of brain tumour diagnosis. We used the BraTS 2020 dataset to segment and classify brain tumours. The U-Net model designed for the project achieved an accuracy rate of 97% with a loss of 47%, DAU-Net reached 90% accuracy with a loss of 33%, DAU-Net 3D achieved 99% accuracy with a loss of 35%, and SGANet achieved 99% accuracy with a loss of 20%, all demonstrating effective outcomes. These find ings aim to improve patient care quality by speeding up medical diagnosis processes using computer-aided technology. Doctors can detect 3D tumours from MRI pictures using software developed as part of the research. The work packages correctly han dled project management throughout the study’s data collection, model creation, and evaluation stages. Regarding brain tumour segmentation, 3D U-Net architecture with multi-head attention mechanisms provides doctors with the best tools for planning surgery and giving each patient the best treatment options. The user-friendly Turkish interface enables simple MRI picture uploads and quick, understandable findings.
dc.identifier.citationTokatlı, N., Bayram, M., Ogur, H., Kılıç, Y., Han, V., Batur, K. C., & Altun, H. (2025). Braindetective: An advanced deep learning application for early detection, segmentation and classification of brain tumours using MRI images. Proceedings of Tenth International Congress on Information and Communication Technology, pp. 35-48. https://doi.org/10.1007/978-981-96-9709-0_3
dc.identifier.doi10.1007/978-981-96-9709-0_3
dc.identifier.endpage48
dc.identifier.isbn9789819697090
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopusqualityQ4
dc.identifier.startpage35
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1190
dc.identifier.urihttps://doi.org/10.1007/978-981-96-9709-0_3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTokatlı, Nazlı
dc.institutionauthorBayram, Mücahit
dc.institutionauthorOgur, Hatice
dc.institutionauthorKılıç, Yusuf
dc.institutionauthorHan, Vesile
dc.institutionauthorBatur, Kutay Can
dc.institutionauthorAltun, Halis
dc.institutionauthorid0000-0001-9840-4211
dc.institutionauthorid0009-0001-0667-3906
dc.institutionauthorid0009-0002-6202-8263
dc.institutionauthorid0009-0009-4492-9405
dc.institutionauthorid0009-0009-1167-2463
dc.institutionauthorid0009-0001-3852-0525
dc.institutionauthorid0000-0002-2126-8757
dc.language.isoen
dc.publisherSpringer Nature Link
dc.relation.ispartofProceedings of Tenth International Congress on Information and Communication Technology
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject3D Brain Tumour Diagnosis
dc.subjectDeep Learning Models
dc.subjectMR Imaging
dc.subjectAI Applications In Turkish Health System
dc.titleBraindetective: An advanced deep learning application for early detection, segmentation and classification of brain tumours using MRI images
dc.typeConference Object
dspace.entity.typePublication

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