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Development of brain tumor segmentation of magnetic resonance imaging (MRI) using u-net deep learning

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dc.citedby5
dc.contributor.authorJwaid W.M.en_US
dc.contributor.authorAl-Hussein Z.S.M.en_US
dc.contributor.authorSabry A.H.en_US
dc.contributor.authorid57201441961en_US
dc.contributor.authorid57220009753en_US
dc.contributor.authorid56602511900en_US
dc.date.accessioned2023-05-29T09:11:11Z
dc.date.available2023-05-29T09:11:11Z
dc.date.issued2021
dc.description.abstractBrain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be timeconsuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset � 2021, Authors. This is an open access article under the Creative Commons CC BY licenseen_US
dc.description.natureFinalen_US
dc.identifier.doi10.15587/1729-4061.2021.238957
dc.identifier.epage31
dc.identifier.issue9-112
dc.identifier.scopus2-s2.0-85116055192
dc.identifier.spage23
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116055192&doi=10.15587%2f1729-4061.2021.238957&partnerID=40&md5=6eb7c8441d9853f54274d1795ae0d257
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26493
dc.identifier.volume4
dc.publisherTechnology Centeren_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEastern-European Journal of Enterprise Technologies
dc.titleDevelopment of brain tumor segmentation of magnetic resonance imaging (MRI) using u-net deep learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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