A Supervised ML Applied Classification Model for Brain Tumors MRI.

Yu, Z; He, Q; Yang, J; Luo, M

A Supervised ML Applied Classification Model for Brain Tumors MRI.

Yu, Z

He, Q Yang, J Luo, M

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Publisher:: Frontiers Media SA
Publication Type:: Journal Article
Citation:: Front Pharmacol, 2022, 13, pp. 884495
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Yu, Z https://orcid.org/0000-0002-8888-4570
dc.contributor.author	He, Q
dc.contributor.author	Yang, J
dc.contributor.author	Luo, M
dc.date.accessioned	2022-08-04T11:55:29Z
dc.date.available	2022-03-28
dc.date.available	2022-08-04T11:55:29Z
dc.date.issued	2022
dc.identifier.citation	Front Pharmacol, 2022, 13, pp. 884495
dc.identifier.issn	1663-9812
dc.identifier.issn	1663-9812
dc.identifier.uri	http://hdl.handle.net/10453/159624
dc.description.abstract	Brain Tumor originates from abnormal cells, which is developed uncontrollably. Magnetic resonance imaging (MRI) is developed to generate high-quality images and provide extensive medical research information. The machine learning algorithms can improve the diagnostic value of MRI to obtain automation and accurate classification of MRI. In this research, we propose a supervised machine learning applied training and testing model to classify and analyze the features of brain tumors MRI in the performance of accuracy, precision, sensitivity and F1 score. The result presents that more than 95% accuracy is obtained in this model. It can be used to classify features more accurate than other existing methods.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Frontiers Media SA
dc.relation.ispartof	Front Pharmacol
dc.relation.isbasedon	10.3389/fphar.2022.884495
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1115 Pharmacology and Pharmaceutical Sciences
dc.title	A Supervised ML Applied Classification Model for Brain Tumors MRI.
dc.type	Journal Article
utslib.citation.volume	13
utslib.location.activity	Switzerland
utslib.for	1115 Pharmacology and Pharmaceutical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Corporate Services)
utslib.copyright.status	open_access	*
dc.date.updated	2022-08-04T11:54:28Z
pubs.publication-status	Published online
pubs.volume	13

Abstract:

Brain Tumor originates from abnormal cells, which is developed uncontrollably. Magnetic resonance imaging (MRI) is developed to generate high-quality images and provide extensive medical research information. The machine learning algorithms can improve the diagnostic value of MRI to obtain automation and accurate classification of MRI. In this research, we propose a supervised machine learning applied training and testing model to classify and analyze the features of brain tumors MRI in the performance of accuracy, precision, sensitivity and F1 score. The result presents that more than 95% accuracy is obtained in this model. It can be used to classify features more accurate than other existing methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/159624