Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/19178
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAlharbi, Hajar Mohammedsaleh Hen
dc.contributor.authorFalzon, Gregoryen
dc.contributor.authorKwan, Paul Hen
dc.date.accessioned2016-06-21T16:42:00Z-
dc.date.issued2015-
dc.identifier.citationProceedings of the Third IAPR Asian Conference on Pattern Recognition (ACPR 2015), p. 221-225en
dc.identifier.isbn9781479961009en
dc.identifier.urihttps://hdl.handle.net/1959.11/19178-
dc.description.abstractThe visual similarity between normal breast tissues and abnormal lesions in digital mammogram images makes computer-aided diagnosis of breast cancer using automatically detected features a highly error-prone task. Our contribution in this paper is a novel feature reduction framework for selecting the most discriminative features that achieves both efficiency and classification accuracy. Our approach applies five individual feature-ranking methods including Fisher score, minimum redundancy-maximum relevance, relief-f, sequential forward feature selection, and genetic algorithm for sorting the extracted features and selecting the features with highest ranking to setup a classifier. Our method achieves an accuracy of 94.27% and a sensitivity of 98.36% with a specificity of 99.27% on a set of 1,100 mammogram patches taken from image retrieval in medical applications database using a neural network classifier, which competes with state-of-the-art classification accuracy 93.11%. Furthermore, we demonstrate that only 49 out of the 119 extracted features are sufficient to achieve the reported accuracy of normal vs. abnormal classification.en
dc.languageenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartofProceedings of the Third IAPR Asian Conference on Pattern Recognition (ACPR 2015)en
dc.titleA novel feature reduction framework for digital mammogram image classificationen
dc.typeConference Publicationen
dc.relation.conferenceACPR 2015: 3rd Asian Conference on Pattern Recognitionen
dc.identifier.doi10.1109/ACPR.2015.7486498en
dc.subject.keywordsPattern Recognition and Data Miningen
dc.subject.keywordsImage Processingen
dc.subject.keywordsCancer Diagnosisen
local.contributor.firstnameHajar Mohammedsaleh Hen
local.contributor.firstnameGregoryen
local.contributor.firstnamePaul Hen
local.subject.for2008080106 Image Processingen
local.subject.for2008111202 Cancer Diagnosisen
local.subject.for2008080109 Pattern Recognition and Data Miningen
local.subject.seo2008920102 Cancer and Related Disordersen
local.subject.seo2008970108 Expanding Knowledge in the Information and Computing Sciencesen
local.subject.seo2008970111 Expanding Knowledge in the Medical and Health Sciencesen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailhalharbi@une.edu.auen
local.profile.emailgfalzon2@une.edu.auen
local.profile.emailwkwan2@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20151118-094032en
local.date.conference3rd - 6th November, 2015en
local.conference.placeKuala Lumpur, Malaysiaen
local.publisher.placeLos Alamitos, United States of Americaen
local.format.startpage221en
local.format.endpage225en
local.peerreviewedYesen
local.contributor.lastnameAlharbien
local.contributor.lastnameFalzonen
local.contributor.lastnameKwanen
dc.identifier.staffune-id:halharbien
dc.identifier.staffune-id:gfalzon2en
dc.identifier.staffune-id:wkwan2en
local.profile.orcid0000-0002-1989-9357en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:19374en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleA novel feature reduction framework for digital mammogram image classificationen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsACPR 2015: 3rd Asian Conference on Pattern Recognition, Kuala Lumpur, Malaysia, 3rd - 6th November, 2015en
local.search.authorAlharbi, Hajar Mohammedsaleh Hen
local.search.authorFalzon, Gregoryen
local.search.authorKwan, Paul Hen
local.uneassociationUnknownen
local.year.published2015en
local.subject.for2020460306 Image processingen
local.subject.for2020321102 Cancer diagnosisen
local.subject.for2020461199 Machine learning not elsewhere classifieden
local.subject.seo2020280115 Expanding knowledge in the information and computing sciencesen
local.date.start2015-11-03-
local.date.end2015-11-06-
Appears in Collections:Conference Publication
Files in This Item:
3 files
File Description SizeFormat 
Show simple item record

SCOPUSTM   
Citations

3
checked on Oct 26, 2024

Page view(s)

1,578
checked on Apr 7, 2024
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.