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Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study

Undoubtedly, early detection and characterization of brain tumor is critical in clinical practices. Automated diagnosis using neuroimaging tool like MRI guided by machine learning approaches has been the focus of numerous researches. In this study, various feature extraction, dimensionality reductio...

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Main Authors: Lim, Jia Qi, Alias, Norma, Johar, Farhana
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/89883/1/LimJiaQi2020_AutomatedClassificationofTypesofBrainTumor.pdf
http://eprints.utm.my/id/eprint/89883/
http://dx.doi.org/10.1063/5.0018056
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id my.utm.89883
record_format eprints
spelling my.utm.898832021-03-04T02:47:45Z http://eprints.utm.my/id/eprint/89883/ Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study Lim, Jia Qi Alias, Norma Johar, Farhana Q Science (General) Undoubtedly, early detection and characterization of brain tumor is critical in clinical practices. Automated diagnosis using neuroimaging tool like MRI guided by machine learning approaches has been the focus of numerous researches. In this study, various feature extraction, dimensionality reduction and supervised classification models are explored, evaluated and compared under different finite number of features to identify the optimal pathway/pipeline for classification of types of brain tumor, namely meningioma, glioma and pituitary tumor. The performance metrics utilized include accuracy, Kappa statistic, sensitivity, precision, F-measure, training time and test time. Results show that RBF SVM (pairwise coupling) under 80 PLS features achieved the highest average accuracy (95.02% ± 0.19%) among all other machine learning pipelines. 2020-10-06 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/89883/1/LimJiaQi2020_AutomatedClassificationofTypesofBrainTumor.pdf Lim, Jia Qi and Alias, Norma and Johar, Farhana (2020) Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study. In: 27th National Symposium on Mathematical Sciences, SKSM 2019, 26 November 2019 - 27 November 2019, Tenera Hotel Bangi, Selangor, Malaysia. http://dx.doi.org/10.1063/5.0018056
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Lim, Jia Qi
Alias, Norma
Johar, Farhana
Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study
description Undoubtedly, early detection and characterization of brain tumor is critical in clinical practices. Automated diagnosis using neuroimaging tool like MRI guided by machine learning approaches has been the focus of numerous researches. In this study, various feature extraction, dimensionality reduction and supervised classification models are explored, evaluated and compared under different finite number of features to identify the optimal pathway/pipeline for classification of types of brain tumor, namely meningioma, glioma and pituitary tumor. The performance metrics utilized include accuracy, Kappa statistic, sensitivity, precision, F-measure, training time and test time. Results show that RBF SVM (pairwise coupling) under 80 PLS features achieved the highest average accuracy (95.02% ± 0.19%) among all other machine learning pipelines.
format Conference or Workshop Item
author Lim, Jia Qi
Alias, Norma
Johar, Farhana
author_facet Lim, Jia Qi
Alias, Norma
Johar, Farhana
author_sort Lim, Jia Qi
title Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study
title_short Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study
title_full Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study
title_fullStr Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study
title_full_unstemmed Automated classification of types of brain tumor in T1-weighted MR images: a thorough comparative study
title_sort automated classification of types of brain tumor in t1-weighted mr images: a thorough comparative study
publishDate 2020
url http://eprints.utm.my/id/eprint/89883/1/LimJiaQi2020_AutomatedClassificationofTypesofBrainTumor.pdf
http://eprints.utm.my/id/eprint/89883/
http://dx.doi.org/10.1063/5.0018056
_version_ 1693725959837974528
score 13.211869

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