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  Published Paper Details:

  Paper Title

  Authors

  V M Ranjith,  Dhanush R,  Shashanka K S

  Keywords

Brain Tumor, MRI images, Convolution Neural Network, Artificial Neural Network, Resnet - 50, CNN with Transfer Learning

  Abstract


A brain tumour is a misshapen cell growth that can be malignant or non-cancerous in nature. The most deadly condition is a brain tumour, which can be detected with automated procedures on MRI images and identified quickly and accurately. Several methods of diagnosis and segmentation of brain tumors have been suggested by many researchers for effective tumor detection. Because of the high diversity in tumor tissue of different patients, automating this process could be a challenging risk. Medical resonance imaging is a challenging and innovative field in the medical science field. In the medical field, the techniques of ML( Machine learning ) and Deep learning holds a significant stand. Brain tumor detection using MRI images have many applications. Brain tumor detection from MRI images is one in all the emerging fields in medical science. There are different brain tumor detection methods. Detection and segmentation methods are used to detect the brain tumor from the MRI images. Here a brief review of the different brain tumor detection methods has been discussed. The different methods include Convolution Neural Network, Artificial Neural Network, Resnet - 50 and CNN with Transfer Learning.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTT020023

  Paper ID - 223624

  Page Number(s) - 112-114

  Pubished in - Volume 10 | Issue 8 | August 2022

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  V M Ranjith,  Dhanush R,  Shashanka K S,   "BRAIN TUMOR DETECTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 8, pp.112-114, August 2022, Available at :http://www.ijcrt.org/papers/IJCRTT020023.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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