Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/4647
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dc.contributor.authorJooravan, Amithen_US
dc.contributor.authorReddy, Serendraen_US
dc.contributor.authorPillay, Nelendranen_US
dc.date.accessioned2023-02-15T10:04:53Z-
dc.date.available2023-02-15T10:04:53Z-
dc.date.issued2022-10-27-
dc.identifier.citationJooravan, A., Reddy, S. and Pillay, N. 2022. Comparative study of binary classifiers for reducing false negative detection of melanoma in skin lesions. Presented at: 2022 International Conference on Engineering and Emerging Technologies (ICEET). doi:10.1109/iceet56468.2022.10007359en_US
dc.identifier.isbn978-1-6654-9106-8-
dc.identifier.urihttps://hdl.handle.net/10321/4647-
dc.description.abstractReliable and accurate classification of a skin lesion is essential to the early diagnosis of skin cancer, especially melanoma. Traditional classification methods require performing a biopsy on the lesion. The overlap of benign and malignant clinical features may lead to incorrect melanoma diagnosis and/or excising an excessive number of benign lesions. This paper focuses on the use of machine learning to aid physicians with the non-invasive classification methodology of skin lesions, whilst prioritising the minimization of false negative classification. The clinical features used are based on the ABCD rule, representing the asymmetry, border, colour and diameter of the lesion. The dermoscopic images chosen are of melanoma lesions less than 0,76mm in thickness which corresponds to the early stages of cancer. The investigated classification methods include K-Nearest neighbours (KNN), Naïve Bayes and linear support vector machine. (LSVM). This research proposes the use of a LSVM machine learning algorithm to classify a skin lesion as being either melanoma or non-melanoma with the lowest false negative rate of the investigated classification. Classification accuracy of 85% and a false negative rate of 5% is achieved.en_US
dc.format.extent6 pen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2022 International Conference on Engineering and Emerging Technologies (ICEET)en_US
dc.subjectSkin canceren_US
dc.subjectImage processingen_US
dc.subjectSupport vector machineen_US
dc.subjectNaïve Bayesen_US
dc.subjectK-nearest neighboursen_US
dc.titleComparative study of binary classifiers for reducing false negative detection of melanoma in skin lesionsen_US
dc.typeConferenceen_US
dc.date.updated2023-02-07T12:43:08Z-
dc.identifier.doi10.1109/iceet56468.2022.10007359-
local.sdgSDG03-
item.openairetypeConference-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith Fulltext-
Appears in Collections:Research Publications (Engineering and Built Environment)
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