Brain tumor segmentation and classification approach for MR images based on convolutional neural networks

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dc.contributor.authorSameer, Mustafa A.
dc.contributor.authorBayat, Oğuz
dc.contributor.authorMohammed, Hussam J.
dc.date.accessioned2021-05-15T12:49:40Z
dc.date.available2021-05-15T12:49:40Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Bilgisayar Mühendisliği Bölümüen_US
dc.description1st International Conference Information Technology to Enhance E-Learning and other Application, IT-ELA 2020 -- 12 July 2020 through 13 July 2020 -- -- 164981
dc.description.abstractBrain tumor considers one of the most dangerous types of cancers as it affects the main nervous system of the human body. Therefore, several techniques using computer vision has been proposed for early diagnoses and avoiding surgical intervention. However, these techniques face challenges in terms of segmentation and classification processes to detect the brain tumor within Magnetic resonance images (MRI). This paper proposes an automated system for detecting and classifying the brain tumor. This system composed of three phases is including enhancement, segmentation, and classification. The enhancement phase utilizes the Adaptive Histogram Equalization (AHE) in order to adjust the MRI images. The segmentation process was achieved using U-NET to segment the abnormal cells from normal brain tissue. The classification phase was conducted using 3D-CNN to classify the brain tumor into a High-Grade Glioma (HGG) and a Low-Grade-Glioma (LGG). Several experiments were conducted to validate the developed system using Brats-2015 dataset. The system achieved 99.7% (Dice Similarity Coefficient) DSC as an accurate rate for segmentation, and 96% and 98.5% an accurate rate using 5-fold and 10-fold, respectively. © 2020 IEEE.en_US
dc.identifier.doi10.1109/IT-ELA50150.2020.9253111
dc.identifier.endpage143en_US
dc.identifier.isbn9781728182339
dc.identifier.scopus2-s2.0-85097801929
dc.identifier.scopusqualityN/A
dc.identifier.startpage138en_US
dc.identifier.urihttps://doi.org/10.1109/IT-ELA50150.2020.9253111
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1088
dc.indekslendigikaynakScopus
dc.institutionauthorBayat, Oğuz
dc.institutionauthorSameer, Mustafa A.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings of 2020 1st Information Technology to Enhance E-Learning and other Application Conference, IT-ELA 2020
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectImage Processingen_US
dc.subjectMedical Imageen_US
dc.subjectSegmentationen_US
dc.titleBrain tumor segmentation and classification approach for MR images based on convolutional neural networks
dc.typeConference Object

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