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dc.contributor.authorSrivastava, Prabhav
dc.contributor.authorMisra, Vanshika
dc.date.accessioned2024-09-19T10:16:00Z
dc.date.available2024-09-19T10:16:00Z
dc.date.issued2023-05
dc.identifier.urihttp://10.10.11.6/handle/1/18207
dc.descriptionSCHOOL OF COMPUTING SCIENCE AND ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING / DEPARTMENT OF COMPUTERAPPLICATION GALGOTIAS UNIVERSITY, GREATER NOIDAen_US
dc.description.abstractConvolutional networks are the most widely used method for egmenting three dimensional (3D) medical images. Deep learning algorithms have recently attained human-level performance in a number of significant application tasks, including lung cancer volumetry and delineation ,preparing for radiation therapy However, cutting-edge topologies like UNet and Deep Medic are computationally intensive and necessitate workstations with graphics processing units for quick inference.en_US
dc.language.isoen_USen_US
dc.publisherGalgotias Universityen_US
dc.subjectbrain tumoren_US
dc.subject3D U Neten_US
dc.titleGlioblastoma brain tumor segmentation and survival prediction using 3D U Neten_US
dc.typeTechnical Reporten_US


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