Multi-class segmentation of brain tumor using Convolution Neural Network

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dc.contributor.advisorBiros, George
dc.creatorAzmat, Muneeza
dc.creator.orcid0000-0002-7784-4100
dc.date.accessioned2018-07-24T20:10:21Z
dc.date.available2018-07-24T20:10:21Z
dc.date.created2018-05
dc.date.issued2018-06-19
dc.date.submittedMay 2018
dc.date.updated2018-07-24T20:10:21Z
dc.description.abstractIn this report a fully Convolution Neural Network (CNN) architecture is used to segment multi-modal Brain Tumors from Magnetic Resonance (MR) images. Due to the challenges in manual segmentation, computerized brain tumor segmentation is one of the most important challenges in medical imaging. The fully convolutional structure of the network makes it faster than any network with a dense fully connected layer. The two phase training and entropy sampling of data makes it easier to learn tumor boundaries and overcome the data imbalance problem.
dc.description.departmentComputational Science, Engineering, and Mathematics
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T27941C1P
dc.identifier.urihttp://hdl.handle.net/2152/65765
dc.language.isoen
dc.subjectBrain tumor
dc.subjectConvolution Neural Network
dc.subjectSegmentation
dc.subjectMulti-class segmentation
dc.subjectMulti-modal brain tumors
dc.subjectMagnetic Resonance images
dc.subjectComputerized brain tumor segmentation
dc.subjectTumor boundaries
dc.subjectData imbalance problem
dc.titleMulti-class segmentation of brain tumor using Convolution Neural Network
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentComputational Science, Engineering, and Mathematics
thesis.degree.disciplineComputational Science, Engineering, and Mathematics
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Computational Science, Engineering, and Mathematics

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