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A Comparison Study of Various Machine Learning Models for Classifying Tumors in Brain MRI

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Parvathy Jyothi, Robert A. Singh

Parvathy Jyothi and Robert A Singh. A Comparison Study of Various Machine Learning Models for Classifying Tumors in Brain MRI. International Journal of Computer Applications 183(47):28-32, January 2022. BibTeX

	author = {Parvathy Jyothi and Robert A. Singh},
	title = {A Comparison Study of Various Machine Learning Models for Classifying Tumors in Brain MRI},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2022},
	volume = {183},
	number = {47},
	month = {Jan},
	year = {2022},
	issn = {0975-8887},
	pages = {28-32},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2022921875},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Magnetic Resonance Imaging is a non-invasive tool used for exploring the internal physique of human body.Machine learning models play a vital role in diagnosing anomalies in early stages so that treatment procedure can be planned according to the category of tumor. In this paper, a comparison study is executed on various machine learning models to classify brain tumors in MR images. For conducting experiments, the data is collected from publicly available dataset. Principal Component Analysis (PCA)is used to extract features from the input brain MR images. The machine learning models classify the images into two categories namely Glioma tumor and Pituitary tumor.


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Support Vector Machine, Random Forest Classifier, Normalization, Gray Level Co-occurrence Matrix, Principal Component Analysis, XGBoost Classifier