Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/55761
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dc.contributor.authorWarsi, Firozen_US
dc.contributor.authorKhanam, Ruqaiyaen_US
dc.contributor.authorKamya, Surajen_US
dc.contributor.authorSuárez Araujo, Carmen Pazen_US
dc.date.accessioned2019-06-11T12:59:48Z-
dc.date.available2019-06-11T12:59:48Z-
dc.date.issued2019en_US
dc.identifier.issn2352-9148en_US
dc.identifier.urihttp://hdl.handle.net/10553/55761-
dc.description.abstractMalignant melanoma is the deadliest form of skin cancer, but can be more readily treated successfully if detected in its early stages. Due to the increasing incidence of melanoma, research in the field of autonomous melanoma detection has accelerated. In this paper, a new method for feature extraction from dermoscopic images, termed multi-direction 3D color-texture feature (CTF), is proposed, and detection is performed using a back propagation multilayer neural network (NN) classifier. The proposed method is tested on the PH 2 dataset (publicly available) in terms of accuracy, sensitivity, and specificity. The extracted combined CTF is fairly discriminative. When it is input and tested in a neural network classifier that is provided, encouraging results are obtained, i.e. accuracy = 97.5%, sensitivity = 98.1% and specificity = 93.84%. Comparative result analyses with other methods are also discussed, and the results are also improved over benchmarking results for the PH2 dataset.en_US
dc.languageengen_US
dc.relation.ispartofInformatics in Medicine Unlockeden_US
dc.sourceInformatics in Medicine Unlocked [ISSN 2352-9148], v. 17 (2019), 100176en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject32 Ciencias médicasen_US
dc.subject220990 Tratamiento digital. Imágenesen_US
dc.subject.otherMelanomaen_US
dc.subject.otherColor texturefeatureen_US
dc.subject.otherDermoscopic imageen_US
dc.subject.otherNeural network classifier and skin canceren_US
dc.titleAn efficient 3D color-texture feature and neural network technique for melanoma detectionen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticlees
dc.identifier.doi10.1016/j.imu.2019.100176
dc.identifier.scopus85064884216
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.authorscopusid57208467728
dc.contributor.authorscopusid56785943200
dc.contributor.authorscopusid56126551300
dc.contributor.authorscopusid6603605708
dc.description.firstpage100176-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgces
item.fulltextCon texto completo-
item.grantfulltextopen-
crisitem.author.deptIUCTC: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-8826-0899-
crisitem.author.parentorgIU de Ciencias y Tecnologías Cibernéticas-
crisitem.author.fullNameSuárez Araujo, Carmen Paz-
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