Hybridization Techniques To Detect Brain Tumor

Diagnosing brain tumor in present era through digital techniques need serious attention as the number of patients are increasing in an awkward manner. Magnetic Resonance Imaging is the tool that is used for detection of brain tumors. This paper is classified in two phases i.e. normal and abnormal b...

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Autores principales:	Muhammad Abrar, Asif Hussain, Roha Masroor, Ifra Masroor
Formato:	article
Lenguaje:	EN
Publicado:	Sukkur IBA University 2021
Materias:	Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/920b0cb336674e08abe69c2df82c6e53
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spelling	oai:doaj.org-article:920b0cb336674e08abe69c2df82c6e532021-11-11T10:07:07ZHybridization Techniques To Detect Brain Tumor10.30537/sjcms.v4i2.6552520-07552522-3003https://doaj.org/article/920b0cb336674e08abe69c2df82c6e532021-01-01T00:00:00Zhttp://localhost:8089/sibajournals/index.php/sjcms/article/view/655https://doaj.org/toc/2520-0755https://doaj.org/toc/2522-3003 Diagnosing brain tumor in present era through digital techniques need serious attention as the number of patients are increasing in an awkward manner. Magnetic Resonance Imaging is the tool that is used for detection of brain tumors. This paper is classified in two phases i.e. normal and abnormal brain images. Then, Feature selection and classification are applied on the given data set. Classification on given data set is done through K- Nearest Neighbor. In the given study, we have taken normal and abnormal samples from Nishtar Medical hospital, Multan. In order to classify brain images, first it needs to pre-process through skull stripping technique then the proposed algorithm is followed. Algorithm involves feature extraction through GLCM and feature selection through ACO. Results have proved its efficiency level up-to 88%. Muhammad AbrarAsif HussainRoha MasroorIfra MasroorSukkur IBA UniversityarticleComputer engineering. Computer hardwareTK7885-7895MathematicsQA1-939Electronic computers. Computer scienceQA75.5-76.95ENSukkur IBA Journal of Computing and Mathematical Sciences, Vol 4, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Computer engineering. Computer hardware TK7885-7895 Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95 Muhammad Abrar Asif Hussain Roha Masroor Ifra Masroor Hybridization Techniques To Detect Brain Tumor
description	Diagnosing brain tumor in present era through digital techniques need serious attention as the number of patients are increasing in an awkward manner. Magnetic Resonance Imaging is the tool that is used for detection of brain tumors. This paper is classified in two phases i.e. normal and abnormal brain images. Then, Feature selection and classification are applied on the given data set. Classification on given data set is done through K- Nearest Neighbor. In the given study, we have taken normal and abnormal samples from Nishtar Medical hospital, Multan. In order to classify brain images, first it needs to pre-process through skull stripping technique then the proposed algorithm is followed. Algorithm involves feature extraction through GLCM and feature selection through ACO. Results have proved its efficiency level up-to 88%.
format	article
author	Muhammad Abrar Asif Hussain Roha Masroor Ifra Masroor
author_facet	Muhammad Abrar Asif Hussain Roha Masroor Ifra Masroor
author_sort	Muhammad Abrar
title	Hybridization Techniques To Detect Brain Tumor
title_short	Hybridization Techniques To Detect Brain Tumor
title_full	Hybridization Techniques To Detect Brain Tumor
title_fullStr	Hybridization Techniques To Detect Brain Tumor
title_full_unstemmed	Hybridization Techniques To Detect Brain Tumor
title_sort	hybridization techniques to detect brain tumor
publisher	Sukkur IBA University
publishDate	2021
url	https://doaj.org/article/920b0cb336674e08abe69c2df82c6e53
work_keys_str_mv	AT muhammadabrar hybridizationtechniquestodetectbraintumor AT asifhussain hybridizationtechniquestodetectbraintumor AT rohamasroor hybridizationtechniquestodetectbraintumor AT iframasroor hybridizationtechniquestodetectbraintumor
_version_	1718439236468736000

Hybridization Techniques To Detect Brain Tumor

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