Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26239
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dc.contributor.authorWajid, Summrinaen_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.authorHuang, Kaizhuen_UK
dc.contributor.authorBoulila, Wadiien_UK
dc.date.accessioned2017-11-30T23:14:05Z-
dc.date.available2017-11-30T23:14:05Z-
dc.date.issued2017-02-23en_UK
dc.identifier.urihttp://hdl.handle.net/1893/26239-
dc.description.abstractThe novel application of Local Energy-based Shape Histogram (LESH) feature extraction technique was recently proposed for breast cancer diagnosis using mammogram images [22]. This paper extends our original work to apply the LESH technique to detect lung cancer. The JSRT Digital Image Database of chest radiographs is selected for research experimentation. Prior to LESH feature extraction, we enhanced the radiograph images using a contrast limited adaptive histogram equalization (CLAHE) approach. Selected state-of-the-art cognitive machine learning classifiers, namely extreme learning machine (ELM), support vector machine (SVM) and echo state network (ESN) are then applied using the LESH extracted features for efficient diagnosis of correct medical state (existence of benign or malignant cancer) in the x-ray images. Comparative simulation results, evaluated using the classification accuracy performance measure, are further bench-marked against state-of-the-art wavelet based features, and authenticate the distinct capability of our proposed framework for enhancing the diagnosis outcome.en_UK
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.relationWajid S, Hussain A, Huang K & Boulila W (2017) Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques. In: 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Palo Alto, CA, USA, 22.08.2016-23.08.2016. Piscataway, NJ, USA: IEEE, pp. 359-366. https://doi.org/10.1109/ICCI-CC.2016.7862060en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. 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.en_UK
dc.rights.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden_UK
dc.subjectEcho State Network (ESN)en_UK
dc.subjectClinical Decision Support Systems (CDSSs)en_UK
dc.subjectLocal Energy based Shape Histogram (LESH)en_UK
dc.subjectExtreme Learning Machine (ELM)en_UK
dc.subjectSupport Vector Machine (SVM)en_UK
dc.titleLung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniquesen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2999-07-01en_UK
dc.rights.embargoreason[07862060.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1109/ICCI-CC.2016.7862060en_UK
dc.citation.spage359en_UK
dc.citation.epage366en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailahu@cs.stir.ac.uken_UK
dc.citation.btitle2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)en_UK
dc.citation.conferencedates2016-08-22 - 2016-08-23en_UK
dc.citation.conferencelocationPalo Alto, CA, USAen_UK
dc.citation.conferencename2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)en_UK
dc.citation.date31/08/2016en_UK
dc.citation.isbn978-1-5090-3845-9en_UK
dc.citation.isbn978-1-5090-3846-6en_UK
dc.publisher.addressPiscataway, NJ, USAen_UK
dc.contributor.affiliationUniversity of Stirlingen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationXi’an Jiaotong Universityen_UK
dc.contributor.affiliationUniversity of Manoubaen_UK
dc.identifier.isiWOS:000405717600047en_UK
dc.identifier.scopusid2-s2.0-85016209357en_UK
dc.identifier.wtid530508en_UK
dc.contributor.orcid0000-0002-8080-082Xen_UK
dc.date.accepted2016-03-01en_UK
dcterms.dateAccepted2016-03-01en_UK
dc.date.filedepositdate2017-11-30en_UK
dc.relation.funderprojectTowards visually-driven speech enhancement for cognitively-inspired multi-modal hearing-aid devicesen_UK
dc.relation.funderrefEP/M026981/1en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorWajid, Summrina|en_UK
local.rioxx.authorHussain, Amir|0000-0002-8080-082Xen_UK
local.rioxx.authorHuang, Kaizhu|en_UK
local.rioxx.authorBoulila, Wadii|en_UK
local.rioxx.projectEP/M026981/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2999-07-01en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||en_UK
local.rioxx.filename07862060.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source978-1-5090-3846-6en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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