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dc.contributor.authorYu, Z
dc.contributor.authorYang, S
dc.contributor.authorZhou, K
dc.contributor.authorAggoun, A
dc.contributor.editorLotfi, Ahmad
dc.contributor.editorBouchachia, Hamid
dc.contributor.editorGegov, Alexander E
dc.contributor.editorLangensiepen, Caroline S
dc.contributor.editorMcGinnity, T Martin
dc.date.accessioned2019-07-16T11:08:22Z
dc.date.available2019-07-16T11:08:22Z
dc.date.issued2018-08-11
dc.identifier.citationYu, Z., Yang, S., Zhou, K. and Aggoun, A. (2018) A low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detection. Advances in Intelligent Systems and Computing, 840, pp. 169-178. (doi:10.1007/978-3-319-97982-3_14)en
dc.identifier.issn2194-5357en
dc.identifier.doi10.1007/978-3-319-97982-3_14en
dc.identifier.urihttp://hdl.handle.net/2436/622563
dc.description.abstract© Springer Nature Switzerland AG 2019. In this paper, we aim to develop a low-computational system for real-time image processing and analysis in endoscopy images for the early detection of the human esophageal adenocarcinoma and colorectal cancer. Rich statistical features are used to train an improved machine-learning algorithm. Our algorithm can achieve a real-time classification of malign and benign cancer tumours with a significantly improved detection precision compared to the classical HOG method as a reference when it is implemented on real time embedded system NVIDIA TX2 platform. Our approach can help to avoid unnecessary biopsies for patients and reduce the over diagnosis of clinically insignificant cancers in the future.en
dc.formatapplication/PDFen
dc.language.isoenen
dc.publisherSpringer International Publishingen
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computingen
dc.relation.urlhttps://doi.org/10.1007/978-3-319-97982-3en
dc.subjectMachine learningen
dc.subjectEndoscopyen
dc.subjectCancer detectionen
dc.subjectTexture analysis divisionen
dc.titleA low computational approach for assistive esophageal adenocarcinoma and colorectal cancer detectionen
dc.typeConference contributionen
dc.identifier.journalAdvances in Intelligent Systems and Computingen
dc.date.updated2019-07-04T11:57:56Z
dc.date.accepted2018-08-11
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW160719AAen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2019-08-11en
dc.source.volume840
dc.source.beginpage169
dc.source.endpage178
dc.description.versionPublished version
refterms.dateFCD2019-07-16T11:04:59Z
refterms.versionFCDAM
refterms.dateFOA2019-07-16T11:08:23Z


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