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Study of Machine Learning vs Deep Learning Algorithms for Detection of Tumor in Human Brain

Dheeraj D.1 , Prasantha H.S.2

Section:Research Paper, Product Type: Journal Paper
Volume-8 , Issue-1 , Page no. 57-63, Jan-2020

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v8i1.5763

Online published on Jan 31, 2020

Copyright © Dheeraj D., Prasantha H.S. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Dheeraj D., Prasantha H.S., “Study of Machine Learning vs Deep Learning Algorithms for Detection of Tumor in Human Brain,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.57-63, 2020.

MLA Style Citation: Dheeraj D., Prasantha H.S. "Study of Machine Learning vs Deep Learning Algorithms for Detection of Tumor in Human Brain." International Journal of Computer Sciences and Engineering 8.1 (2020): 57-63.

APA Style Citation: Dheeraj D., Prasantha H.S., (2020). Study of Machine Learning vs Deep Learning Algorithms for Detection of Tumor in Human Brain. International Journal of Computer Sciences and Engineering, 8(1), 57-63.

BibTex Style Citation:
@article{D._2020,
author = {Dheeraj D., Prasantha H.S.},
title = {Study of Machine Learning vs Deep Learning Algorithms for Detection of Tumor in Human Brain},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2020},
volume = {8},
Issue = {1},
month = {1},
year = {2020},
issn = {2347-2693},
pages = {57-63},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4996},
doi = {https://doi.org/10.26438/ijcse/v8i1.5763}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i1.5763}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4996
TI - Study of Machine Learning vs Deep Learning Algorithms for Detection of Tumor in Human Brain
T2 - International Journal of Computer Sciences and Engineering
AU - Dheeraj D., Prasantha H.S.
PY - 2020
DA - 2020/01/31
PB - IJCSE, Indore, INDIA
SP - 57-63
IS - 1
VL - 8
SN - 2347-2693
ER -

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Abstract

Modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Brain tumor is an abnormal mass of tissue in which some cells grow and multiply uncontrollably, apparently unregulated by the mechanisms that control normal cells. There are three types of tumor that are commonly observed viz. Benign, Pre-Malignant, and Malignant. Many supervised and unsupervised classification algorithms are used for detection of tumor as benign or malignant. Usually lighter datasets are used for image classification in application field where as comparatively larger and heavier datasets are used in case of medical field. Many parameters chosen during training play a very important role in measuring the performance and accuracy of the system. Thus an attempt has been made to clearly show how accuracy of the algorithm varies based on the parameters chosen for detection of brain tumor in human brain for an MRI image.

Key-Words / Index Term

CNN, Transfer Learning, Medical Imaging, Glioma, Image Classification, Machine Learning, Deep Learning

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