dc.contributor.author | Srivastava, Prabhav | |
dc.contributor.author | Misra, Vanshika | |
dc.date.accessioned | 2024-09-19T10:16:00Z | |
dc.date.available | 2024-09-19T10:16:00Z | |
dc.date.issued | 2023-05 | |
dc.identifier.uri | http://10.10.11.6/handle/1/18207 | |
dc.description | SCHOOL OF COMPUTING SCIENCE AND ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING /
DEPARTMENT OF COMPUTERAPPLICATION
GALGOTIAS UNIVERSITY, GREATER NOIDA | en_US |
dc.description.abstract | Convolutional 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.iso | en_US | en_US |
dc.publisher | Galgotias University | en_US |
dc.subject | brain tumor | en_US |
dc.subject | 3D U Net | en_US |
dc.title | Glioblastoma brain tumor segmentation and survival prediction using 3D U Net | en_US |
dc.type | Technical Report | en_US |