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dc.contributor.authorWajid, Summrinaen_UK
dc.contributor.authorHussain, Amiren_UK
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.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.
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
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.citation.jtitleExpert Systems with Applicationsen_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.funderDigital Health Instituteen_UK
dc.contributor.funderThe British Councilen_UK
dc.contributor.affiliationUniversity of Stirlingen_UK
dc.contributor.affiliationComputing Scienceen_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
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_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
Appears in Collections:Computing Science and Mathematics Journal Articles

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