A Lightweight Deep Model for Brain Tumor Segmentation

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dc.authoridÖksüz, Coşku/0000-0001-7116-2734
dc.authorwosidÖksüz, Coşku/AAH-3944-2021
dc.contributor.authorOksuz, Cosku
dc.contributor.authorUrhan, Oguzhan
dc.contributor.authorGullu, Mehmet Kemal
dc.date.accessioned2024-03-09T18:48:33Z
dc.date.available2024-03-09T18:48:33Z
dc.date.issued2021
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORKen_US
dc.description.abstractBrain tumors are one of the major causes of increasing deaths worldwide. It is important to correctly identify cancerous tissues by experts in order to make correct treatment planning and to increase patient survival rates. However, manually tracking and segmentation of cancerous tissues in many sections of volumetric MR data is an error-prone and time-consuming process. Developments in the field of deep learning in recent years allow the tasks performed by humans to be performed with higher accuracy and speeds through the developed automatic systems. In this study, a deep learning-based light-weighted model with 6.78M parameters is proposed for the classification of cancerous tissues in the brain. Cross-validation of the proposed method on a public data set results in 84.61%, 82.54%, and 87.15% Boundary F-1, mean IoU, and mean accuracy, respectively, shows the robustness of the proposed model.en_US
dc.description.sponsorshipIEEE,IEEE Turkey Secten_US
dc.identifier.doi10.1109/SIU53274.2021.9477794
dc.identifier.isbn978-1-6654-3649-6
dc.identifier.scopus2-s2.0-85111470990en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/SIU53274.2021.9477794
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1385
dc.identifier.wosWOS:000808100700037en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof29th Ieee Conference on Signal Processing and Communications Applications (Siu 2021)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSegmentation; Mri; Brain Tumor; Computer Aided Diagnosisen_US
dc.titleA Lightweight Deep Model for Brain Tumor Segmentationen_US
dc.typeConference Objecten_US

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