Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31430
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dc.contributor.authorAli, Abder-Rahmanen_UK
dc.contributor.authorLi, Jingpengen_UK
dc.contributor.authorKanwal, Summrinaen_UK
dc.contributor.authorYang, Guangen_UK
dc.contributor.authorHussain, Amiren_UK
dc.contributor.authorO'Shea, Sally Janeen_UK
dc.date.accessioned2020-07-14T00:13:34Z-
dc.date.available2020-07-14T00:13:34Z-
dc.date.issued2020en_UK
dc.identifier.other297en_UK
dc.identifier.urihttp://hdl.handle.net/1893/31430-
dc.description.abstractSkin lesion border irregularity, which represents the B feature in the ABCD rule, is considered one of the most significant factors in melanoma diagnosis. Since signs that clinicians rely on in melanoma diagnosis involve subjective judgment including visual signs such as border irregularity, this deems it necessary to develop an objective approach to finding border irregularity. Increased research in neural networks has been carried out in recent years mainly driven by the advances of deep learning. Artificial neural networks (ANNs) or multilayer perceptrons have been shown to perform well in supervised learning tasks. However, such networks usually don't incorporate information pertaining the ambiguity of the inputs when training the network, which in turn could affect how the weights are being updated in the learning process and eventually degrading the performance of the network when applied on test data. In this paper, we propose a fuzzy multilayer perceptron (F-MLP) that takes the ambiguity of the inputs into consideration and subsequently reduces the effects of ambiguous inputs on the learning process. A new optimization function, the fuzzy gradient descent, has been proposed to reflect those changes. Moreover, a type-II fuzzy sigmoid activation function has also been proposed which enables finding the range of performance the fuzzy neural network is able to attain. The fuzzy neural network was used to predict the skin lesion border irregularity, where the lesion was firstly segmented from the skin, the lesion border extracted, border irregularity measured using a proposed measure vector, and using the extracted border irregularity measures to train the neural network. The proposed approach outperformed most of the state-of-the-art classification methods in general and its standard neural network counterpart in particular. However, the proposed fuzzy neural network was more time-consuming when training the network.en_UK
dc.language.isoenen_UK
dc.publisherFrontiers Media SAen_UK
dc.relationAli A, Li J, Kanwal S, Yang G, Hussain A & O'Shea SJ (2020) A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images. Frontiers in Medicine, 7, Art. No.: 297. https://doi.org/10.3389/fmed.2020.00297en_UK
dc.rights© 2020 Ali, Li, Kanwal, Yang, Hussain and Jane O'Shea. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectmelanomaen_UK
dc.subjectirregularityen_UK
dc.subjectdermoscopyen_UK
dc.subjectmultilayer perceptronen_UK
dc.subjectfuzzy logicen_UK
dc.titleA Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Imagesen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.3389/fmed.2020.00297en_UK
dc.identifier.pmid32733903en_UK
dc.citation.jtitleFrontiers in Medicineen_UK
dc.citation.issn2296-858Xen_UK
dc.citation.volume7en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailjingpeng.li@stir.ac.uken_UK
dc.citation.date07/07/2020en_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationSaudi Electronic Universityen_UK
dc.contributor.affiliationImperial College Londonen_UK
dc.contributor.affiliationEdinburgh Napier Universityen_UK
dc.contributor.affiliationMater Private Hospitalen_UK
dc.identifier.isiWOS:000552490000001en_UK
dc.identifier.scopusid2-s2.0-85084920768en_UK
dc.identifier.wtid1643188en_UK
dc.contributor.orcid0000-0002-5450-5472en_UK
dc.contributor.orcid0000-0002-6758-0084en_UK
dc.date.accepted2020-05-26en_UK
dcterms.dateAccepted2020-05-26en_UK
dc.date.filedepositdate2020-07-10en_UK
rioxxterms.apcpaiden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorAli, Abder-Rahman|0000-0002-5450-5472en_UK
local.rioxx.authorLi, Jingpeng|0000-0002-6758-0084en_UK
local.rioxx.authorKanwal, Summrina|en_UK
local.rioxx.authorYang, Guang|en_UK
local.rioxx.authorHussain, Amir|en_UK
local.rioxx.authorO'Shea, Sally Jane|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2020-07-10en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2020-07-10|en_UK
local.rioxx.filenamefmed-07-00297.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source2296-858Xen_UK
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