Open AccessBrain Tumor Identification and Locating
Author(s) -
Sanjeev Sriram
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.38440
Subject(s) - glioblastoma , brain tumor , identification (biology) , segmentation , computer science , lift (data mining) , neuroimaging , medicine , medical physics , artificial intelligence , psychology , neuroscience , pathology , machine learning , cancer research , botany , biology
Various poorer-grade glioblastoma subtypes related with outline attribute have been identified in a late research. poorer-grade glioblastoma can have symptoms ranging from slight seizures to extensive seizures, affecting the capability to talk or even lift your arms and legs. This poorer-grade glioblastoma is handled with a combination of surgery and examination by monitoring the tumor with brain MRI scans. The study of this project enabled us to build a completely self-working system for segmentation of tumor utilizing computer vision techniques, and deploying models that would allow high- quality LGG detection in the brain MRI would potentially be self-working to identify the genomic subtype of the tumor by quick and low-cost imaging. The methodology, procedures, pros and their boundaries and their future ultimatums are discussed in this project.