Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/23798
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dc.contributor.authorWajid, Summrinaen_UK
dc.contributor.authorHussain, Amiren_UK
dc.date.accessioned2016-07-16T00:17:04Z-
dc.date.available2016-07-16T00:17:04Z-
dc.date.issued2015-11-15en_UK
dc.identifier.urihttp://hdl.handle.net/1893/23798-
dc.description.abstractThis paper proposes a novel local energy-based shape histogram (LESH) as the feature set for recognition of abnormalities in mammograms. It investigates the implication of this technique on mammogram datasets of the Mammographic Image Analysis Society and INbreast. In the evaluation, regions of interest were extracted from the mammograms, their LESH features calculated, and fed to support vector machine (SVM) classifiers. In addition, the impact of selecting a subset of LESH features on classification performance was also observed and benchmarked against a state-of-the-art wavelet based feature extraction method. The proposed method achieved a higher classification accuracy of 99.00±0.50, as well as an Az value of 0.9900±0.0050 with multiple SVM kernels, where a linear kernel performed with 100% accuracy for distinguishing between the abnormalities (masses vs. microcalcifications). Hence, the general capability of the proposed method was established, in which it not only distinguishes between malignant and benign cases for any type of abnormality but also among different types of abnormalities. It is therefore concluded that LESH features are an excellent choice for extracting significant clinical information from mammogram images with significant potential for application to 3-D MRI images.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationWajid S & Hussain A (2015) Local energy-based shape histogram feature extraction technique for breast cancer diagnosis. Expert Systems with Applications, 42 (20), pp. 6990-6999. https://doi.org/10.1016/j.eswa.2015.04.057en_UK
dc.rightsThis item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Wajid S & Hussain A (2015) Local energy-based shape histogram feature extraction technique for breast cancer diagnosis, Expert Systems with Applications, 42 (20), pp. 6990-6999. DOI: 10.1016/j.eswa.2015.04.057 © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.subjectComputer-aided decision support system (CADSS)en_UK
dc.subjectLocal energy-based shape histogram (LESH)en_UK
dc.subjectSupport vector machine (SVM)en_UK
dc.subjectLocal energy modelen_UK
dc.subjectReceiver operating characteristic (ROC) curveen_UK
dc.titleLocal energy-based shape histogram feature extraction technique for breast cancer diagnosisen_UK
dc.typeJournal Articleen_UK
dc.rights.embargoreason[Elsevier-journal-Expert-Systems-Applications-2015-published-LESH-Classification-paper-final.pdf] Publisher requires embargo of 12 months after formal publication.en_UK
dc.identifier.doi10.1016/j.eswa.2015.04.057en_UK
dc.citation.jtitleExpert Systems with Applicationsen_UK
dc.citation.issn0957-4174en_UK
dc.citation.volume42en_UK
dc.citation.issue20en_UK
dc.citation.spage6990en_UK
dc.citation.epage6999en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderDigital Health Instituteen_UK
dc.contributor.funderThe British Councilen_UK
dc.author.emailahu@cs.stir.ac.uken_UK
dc.citation.date01/05/2015en_UK
dc.contributor.affiliationUniversity of Stirlingen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.identifier.isiWOS:000357230000014en_UK
dc.identifier.scopusid2-s2.0-84930644499en_UK
dc.identifier.wtid885118en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.accepted2015-03-24en_UK
dcterms.dateAccepted2015-03-24en_UK
dc.date.filedepositdate2016-07-14en_UK
dc.relation.funderprojectTowards a cognitive vision-based Mulit-agent Modelling and control Frameworken_UK
dc.relation.funderprojectA disruptive patient centric preventive diabetes (Type 2) app PD2Aen_UK
dc.relation.funderrefn/aen_UK
dc.relation.funderrefPD2Aen_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionAMen_UK
local.rioxx.authorWajid, Summrina|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.projectn/a|The British Council|en_UK
local.rioxx.projectPD2A|Digital Health Institute|en_UK
local.rioxx.freetoreaddate2016-11-02en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2016-11-01en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by-nc-nd/4.0/|2016-11-02|en_UK
local.rioxx.filenameElsevier-journal-Expert-Systems-Applications-2015-published-LESH-Classification-paper-final.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0957-4174en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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