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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/997
DC FieldValueLanguage
dc.contributor.authorDoan Thai Thien Locen_US
dc.date.accessioned2021-03-09T03:10:28Z-
dc.date.available2021-03-09T03:10:28Z-
dc.date.issued2020-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/997-
dc.description.abstractBrain tumour is a very hard to treat disease. It is dangerous and requires a lot of expertise to medicate. Brain experts need the tumour segmentation to keep track of the effectiveness of the treatment. Manual segmentation labeling can be time-consuming and error-prone due to human inaccuracy. Automated segmentation of brain tumour using 3D magnetic resonance images (MRI) is favored for diagnosis, treatment controlling and method planning. Lately, deep learning have been exceeding human accuracy in highly conceptual task like brain tumour segmentation. In this paper, I will introduce some deep learning networks using DenseNet, a recent success deep learning method for the task brain tumour segmentatio-
dc.language.isoen-
dc.publisherVietnamese-German University-
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectArtificial intelligence-
dc.subjectMachine learning-
dc.subjectDeep learning-
dc.titleBrain tumour segmentation using dense convolutional neural network-
dc.typeThesis-
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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