An Evaluation of Image Preprocessing in Skin Lesions Detection

Silva, Giuliana Martins; Lazzaretti, André E.; Monteiro, Fernando C.

Publication

An Evaluation of Image Preprocessing in Skin Lesions Detection

2024Conference paper

dc.contributor.author	Silva, Giuliana Martins
dc.contributor.author	Lazzaretti, André E.
dc.contributor.author	Monteiro, Fernando C.
dc.date.accessioned	2024-10-08T15:40:53Z
dc.date.available	2024-10-08T15:40:53Z
dc.date.issued	2024
dc.description.abstract	This study aims to evaluate the impact of image preprocessing techniques on the performance of Convolutional Neural Networks (CNNs) in the task of skin lesion classification. The study is made on the ISIC 2017 dataset, a widely used resource in skin cancer diagnosis research. Thirteen popular CNN models were trained using transfer learning. An ensemble strategy was also employed to generate a final diagnosis based on the classifications of different models. The results indicate that image preprocessing can significantly enhance the performance of CNN models in skin lesion classification tasks. Our best model obtained a balanced accuracy of 0.7879.	pt_PT
dc.description.sponsorship	The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Silva, Giuliana M.; Lazzaretti, André E.; Monteiro, Fernando C. (2024). An Evaluation of Image Preprocessing in Skin Lesions Detection. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 35–49. ISBN 978-3-031-53035-7	pt_PT
dc.identifier.doi	10.1007/978-3-031-53036-4_3	pt_PT
dc.identifier.isbn	978-3-031-53035-7
dc.identifier.isbn	978-3-031-53036-4
dc.identifier.uri	http://hdl.handle.net/10198/30386
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Springer Nature	pt_PT
dc.relation	Research Centre in Digitalization and Intelligent Robotics
dc.relation	Research Centre in Digitalization and Intelligent Robotics
dc.relation	Associate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Skin Lesion Classification	pt_PT
dc.subject	Convolutional Neural Networks	pt_PT
dc.subject	Deep Learning	pt_PT
dc.subject	Image Preprocessing	pt_PT
dc.title	An Evaluation of Image Preprocessing in Skin Lesions Detection	pt_PT
dc.type	conference paper
dspace.entity.type	Publication
oaire.awardTitle	Research Centre in Digitalization and Intelligent Robotics
oaire.awardTitle	Research Centre in Digitalization and Intelligent Robotics
oaire.awardTitle	Associate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage	49	pt_PT
oaire.citation.startPage	35	pt_PT
oaire.citation.title	3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)	pt_PT
oaire.fundingStream	6817 - DCRRNI ID
oaire.fundingStream	6817 - DCRRNI ID
oaire.fundingStream	6817 - DCRRNI ID
person.familyName	Monteiro
person.givenName	Fernando C.
person.identifier.ciencia-id	2019-BDBF-10E2
person.identifier.orcid	0000-0002-1421-8006
person.identifier.rid	H-9213-2016
person.identifier.scopus-author-id	8986162600
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.name	Fundação para a Ciência e a Tecnologia
project.funder.name	Fundação para a Ciência e a Tecnologia
project.funder.name	Fundação para a Ciência e a Tecnologia
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