Particle swarm optimization based feature selection in mammogram mass classification

Wong, MT; He, X; Nguyen, H; Yeh, WC

Particle swarm optimization based feature selection in mammogram mass classification

Wong, MT He, X

Nguyen, H

Yeh, WC

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Publication Type:: Conference Proceeding
Citation:: ICCH 2012 Proceedings - International Conference on Computerized Healthcare, 2012, pp. 152 - 157
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Wong, MT	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Yeh, WC	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	ICCH 2012 Proceedings - International Conference on Computerized Healthcare, 2012, pp. 152 - 157	en_US
dc.identifier.isbn	9781467351294	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32814
dc.description.abstract	Mammography is currently the most effective method for early detection of breast cancer. This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal tissue regions by first finding the significant texture features of ROI using binary particle swarm optimization (BPSO). The data set used consisted of sixty-nine ROIs from the MIAS Mini-Mammographic database. Eighteen texture features were derived from the gray level co-occurrence matrix (GLCM) of each ROI. Significant features are found by a feature selection technique based on BPSO. The decision tree classifier is then used to classify the test set using these significant features. Experimental results show that the significant texture features found by the BPSO based feature selection technique can have better classification accuracy when compared to the full set of features. The BPSO feature selection technique also has similar or better performance in classification accuracy when compared to other widely used existing techniques. © 2012 IEEE.	en_US
dc.relation.ispartof	ICCH 2012 Proceedings - International Conference on Computerized Healthcare	en_US
dc.relation.isbasedon	10.1109/ICCH.2012.6724487	en_US
dc.title	Particle swarm optimization based feature selection in mammogram mass classification	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Mammography is currently the most effective method for early detection of breast cancer. This paper proposes an effective technique to classify regions of interests (ROIs) of digitized mammograms into mass and normal tissue regions by first finding the significant texture features of ROI using binary particle swarm optimization (BPSO). The data set used consisted of sixty-nine ROIs from the MIAS Mini-Mammographic database. Eighteen texture features were derived from the gray level co-occurrence matrix (GLCM) of each ROI. Significant features are found by a feature selection technique based on BPSO. The decision tree classifier is then used to classify the test set using these significant features. Experimental results show that the significant texture features found by the BPSO based feature selection technique can have better classification accuracy when compared to the full set of features. The BPSO feature selection technique also has similar or better performance in classification accuracy when compared to other widely used existing techniques. © 2012 IEEE.

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http://hdl.handle.net/10453/32814