2026-10-27T23:47:11Zhttps://kostu-rdlopen.rawdatalibrary.net/oai/request

oai:kostu-rdlopen.rawdatalibrary.net:kostu/102192026-10-27T20:41:06Zopenairecom_kostu_10000col_kostu_10001

Deep Learning and Transfer Learning Based Brain Tumor Segmentation; [Derin Ogrenme ve Transfer Ogrenme Tabanli Beyin Tumoru Segmentasyonu]Eker, Ayse GulPehlivanoglu, Meltem KurtInce, IbrahimDuru, Nevcihanbrain tumordeep learningtransfer learningtumor segmentationIn brain tumors, which are formed by the abnormal growth of cells in brain tissue, early diagnosis, and accurate treatment are crucial. Brain tumor segmentation provides experts with the ability to accurately identify brain tumors and determine their location. Through segmentation, the type, size, and shape of tumors can be determined, enabling the application of appropriate treatment. In this study, the aim was to perform brain tumor segmentation on the Brain Tumor Figshare (BTF) dataset, which includes diverse images in terms of structural complexity, viewing angles, different device usage, noise, and bias field effects, without applying any preprocessing. Basic models such as U-Net and FCN were utilized, along with transfer learning-based approaches using VGG, XceptionNet, InceptionNet, and ResNet architectures as the underlying structures in these models. According to the experimental results, the 16-layer FCN architecture with VGG- 19 as the underlying structure achieved the highest score with a dice score of 0.9169.Institute of Electrical and Electronics Engineers Inc.2023Conference Objecthttps://rdlopen.rawdatalibrary.net/r/kostu/10219https://doi.org/10.1109/ubmk59864.2023.10286591979-835034081-5trUBMK 2023 - Proceedings: 8th International Conference on Computer Science and Engineeringinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/conferenceObjecthttps://kostu-rdlopen.rawdatalibrary.net/tr/items/kostu/10219