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Automatic brain tumor detection using feature selection and machine learning from MRI Images

A brain tumor is a group of defective cells in the brain. It happens when a cell in the brain develops a dysfunctional structure. Nowadays it becoming a crucial factor of death for a large number of people. Among all the varieties of tumors, the seriousness of a brain tumor is high. Therefore, insta...

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Main Authors: Shafi, A. S.M., Hasan, Md Mahmudul, Molla, M. M.Imran, Alam, Mohammad Khurshed, Islam, Md Tarequl
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39517/1/Automatic%20Brain%20Tumor%20Detection%20Using%20Feature%20Selection%20and%20Machine.pdf
http://umpir.ump.edu.my/id/eprint/39517/2/Automatic%20brain%20tumor%20detection%20using%20feature%20selection%20and%20machine%20learning%20from%20MRI%20Images_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39517/
https://doi.org/10.1007/978-981-16-8690-0_66
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spelling my.ump.umpir.395172023-12-06T01:32:49Z http://umpir.ump.edu.my/id/eprint/39517/ Automatic brain tumor detection using feature selection and machine learning from MRI Images Shafi, A. S.M. Hasan, Md Mahmudul Molla, M. M.Imran Alam, Mohammad Khurshed Islam, Md Tarequl T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering A brain tumor is a group of defective cells in the brain. It happens when a cell in the brain develops a dysfunctional structure. Nowadays it becoming a crucial factor of death for a large number of people. Among all the varieties of tumors, the seriousness of a brain tumor is high. Therefore, instant detection and proper care to be done to save a life from brain tumors. Microscopic examination can separate the tumor cells from healthy cells. They are typically less well separated than normal cells. In modern imaging technology, the detection and classification of brain tumors is a primary concern. For a clinical supervisor or radiologist, it is time-consuming and frustrating work. The accuracy of recognition and classification of tumors executed by radiologists or clinical experts is depended on their experience only. Therefore, accurate identification and classification of brain tumors can be determined by image processing techniques. This research suggests a machine learning module to detect brain tumors using magnetic resonance imaging (MRI) of brain tumors. The method consists of pre-processing of nearly raw raster data (NRRD) of the MRI images, feature extraction, feature selection, and the classification learner to evaluate and construct the final model. The classification learner is designed with a support vector machine (SVM) classifier. The classification method performs well with weighted sensitivity, specificity, precision, and accuracy of 98.81%, 98.88%, 98.82%, and 98.81% respectively. The findings may infer a remarkable step for detecting the presence of tumors in neuro-medicine diagnosis. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39517/1/Automatic%20Brain%20Tumor%20Detection%20Using%20Feature%20Selection%20and%20Machine.pdf pdf en http://umpir.ump.edu.my/id/eprint/39517/2/Automatic%20brain%20tumor%20detection%20using%20feature%20selection%20and%20machine%20learning%20from%20MRI%20Images_ABS.pdf Shafi, A. S.M. and Hasan, Md Mahmudul and Molla, M. M.Imran and Alam, Mohammad Khurshed and Islam, Md Tarequl (2022) Automatic brain tumor detection using feature selection and machine learning from MRI Images. In: Lecture Notes in Electrical Engineering; 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021, 23 August 2021 , Kuantan, Pahang. pp. 751-762., 842 (274719). ISSN 1876-1100 ISBN 978-981168689-4 https://doi.org/10.1007/978-981-16-8690-0_66
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Shafi, A. S.M.
Hasan, Md Mahmudul
Molla, M. M.Imran
Alam, Mohammad Khurshed
Islam, Md Tarequl
Automatic brain tumor detection using feature selection and machine learning from MRI Images
description A brain tumor is a group of defective cells in the brain. It happens when a cell in the brain develops a dysfunctional structure. Nowadays it becoming a crucial factor of death for a large number of people. Among all the varieties of tumors, the seriousness of a brain tumor is high. Therefore, instant detection and proper care to be done to save a life from brain tumors. Microscopic examination can separate the tumor cells from healthy cells. They are typically less well separated than normal cells. In modern imaging technology, the detection and classification of brain tumors is a primary concern. For a clinical supervisor or radiologist, it is time-consuming and frustrating work. The accuracy of recognition and classification of tumors executed by radiologists or clinical experts is depended on their experience only. Therefore, accurate identification and classification of brain tumors can be determined by image processing techniques. This research suggests a machine learning module to detect brain tumors using magnetic resonance imaging (MRI) of brain tumors. The method consists of pre-processing of nearly raw raster data (NRRD) of the MRI images, feature extraction, feature selection, and the classification learner to evaluate and construct the final model. The classification learner is designed with a support vector machine (SVM) classifier. The classification method performs well with weighted sensitivity, specificity, precision, and accuracy of 98.81%, 98.88%, 98.82%, and 98.81% respectively. The findings may infer a remarkable step for detecting the presence of tumors in neuro-medicine diagnosis.
format Conference or Workshop Item
author Shafi, A. S.M.
Hasan, Md Mahmudul
Molla, M. M.Imran
Alam, Mohammad Khurshed
Islam, Md Tarequl
author_facet Shafi, A. S.M.
Hasan, Md Mahmudul
Molla, M. M.Imran
Alam, Mohammad Khurshed
Islam, Md Tarequl
author_sort Shafi, A. S.M.
title Automatic brain tumor detection using feature selection and machine learning from MRI Images
title_short Automatic brain tumor detection using feature selection and machine learning from MRI Images
title_full Automatic brain tumor detection using feature selection and machine learning from MRI Images
title_fullStr Automatic brain tumor detection using feature selection and machine learning from MRI Images
title_full_unstemmed Automatic brain tumor detection using feature selection and machine learning from MRI Images
title_sort automatic brain tumor detection using feature selection and machine learning from mri images
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39517/1/Automatic%20Brain%20Tumor%20Detection%20Using%20Feature%20Selection%20and%20Machine.pdf
http://umpir.ump.edu.my/id/eprint/39517/2/Automatic%20brain%20tumor%20detection%20using%20feature%20selection%20and%20machine%20learning%20from%20MRI%20Images_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39517/
https://doi.org/10.1007/978-981-16-8690-0_66
_version_ 1822923908698865664
score 13.232414

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