Publicação
An Evaluation of Image Preprocessing in Skin Lesions Detection
| Resumo: | 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. |
|---|---|
| Autores principais: | Silva, Giuliana Martins |
| Outros Autores: | Lazzaretti, André E.; Monteiro, Fernando C. |
| Assunto: | Skin Lesion Classification Convolutional Neural Networks Deep Learning Image Preprocessing |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867172730924695552 |
|---|---|
| author | Silva, Giuliana Martins |
| author2 | Lazzaretti, André E. Monteiro, Fernando C. |
| author2_role | author author |
| author_facet | Silva, Giuliana Martins Lazzaretti, André E. Monteiro, Fernando C. |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Silva, Giuliana Martins\"},{\"Person.name\":\"Lazzaretti, André E.\"},{\"Person.name\":\"Monteiro, Fernando C.\",\"Person.identifier.orcid\":\"0000-0002-1421-8006\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Silva, Giuliana Martins Lazzaretti, André E. Monteiro, Fernando C. |
| datacite.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-10-08T15:40:53Z |
| datacite.date.embargoed.fl_str_mv | 2024-10-08T15:40:53Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Skin Lesion Classification Convolutional Neural Networks Deep Learning Image Preprocessing |
| datacite.titles.title.fl_str_mv | An Evaluation of Image Preprocessing in Skin Lesions Detection |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Silva, Giuliana Martins Lazzaretti, André E. Monteiro, Fernando C. |
| dc.date.Accepted.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-10-08T15:40:53Z |
| dc.date.embargoed.fl_str_mv | 2024-10-08T15:40:53Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/30386 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Springer Nature |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Skin Lesion Classification Convolutional Neural Networks Deep Learning Image Preprocessing |
| dc.title.fl_str_mv | An Evaluation of Image Preprocessing in Skin Lesions Detection |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | 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. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/8f66963c-5c88-48cf-835d-52d563521759/download |
| funding.funder.alternateName_str_mv | FCT FCT FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia Fundação para a Ciência e a Tecnologia |
| funding.name_str_mv | 6817 - DCRRNI ID 6817 - DCRRNI ID 6817 - DCRRNI ID |
| id | ipb_d05e4e928cc580eb712098a88e9867f0 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/30386 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/30386 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Silva, Giuliana Martins Lazzaretti, André E. Monteiro, Fernando C. Monteiro, Fernando C. https://www.ciencia-id.pt/2019-BDBF-10E2 2019-BDBF-10E2 http://orcid.org/0000-0002-1421-8006 0000-0002-1421-8006 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | Springer Nature |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engSpringer Naturept_PTThis 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.application/pdfpt_PTAn Evaluation of Image Preprocessing in Skin Lesions DetectionSilva, Giuliana MartinsLazzaretti, André E.PersonalMonteiro, Fernando C.DSpacehttp://dspace.org/items/363b6c37-282c-4cd6-bb54-3c97cc700d78DSpacehttp://dspace.org/items/363b6c37-282c-4cd6-bb54-3c97cc700d78MonteiroFernando C.Ciência IDhttps://www.ciencia-id.pt2019-BDBF-10E2ORCIDhttp://orcid.org0000-0002-1421-8006Researcher IDhttps://www.researcherid.comH-9213-2016Scopus Author IDhttps://www.scopus.com8986162600HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-3-031-53035-7ISBNIsPartOf978-3-031-53036-4DOIIsPartOf10.1007/978-3-031-53036-4_32024-10-08T15:40:53Z20242024-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/30386http://purl.org/coar/access_right/c_16ecrestricted accessSkin Lesion ClassificationConvolutional Neural NetworksDeep LearningImage Preprocessing1682078 bytesFundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Robotics6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaResearch Centre in Digitalization and Intelligent Robotics6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871Fundação para a Ciência e a TecnologiaAssociate Laboratory for Sustainability and Tecnology in Mountain Regions6817 - DCRRNI IDCrossref Funder IDhttp://doi.org/10.13039/501100001871other research producthttp://purl.org/coar/resource_type/c_5794conference paper2024http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/8f66963c-5c88-48cf-835d-52d563521759/download3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023)3549 |
| spellingShingle | An Evaluation of Image Preprocessing in Skin Lesions Detection Silva, Giuliana Martins Skin Lesion Classification Convolutional Neural Networks Deep Learning Image Preprocessing |
| status | SINGLETON |
| subject.fl_str_mv | Skin Lesion Classification Convolutional Neural Networks Deep Learning Image Preprocessing |
| title | An Evaluation of Image Preprocessing in Skin Lesions Detection |
| title_full | An Evaluation of Image Preprocessing in Skin Lesions Detection |
| title_fullStr | An Evaluation of Image Preprocessing in Skin Lesions Detection |
| title_full_unstemmed | An Evaluation of Image Preprocessing in Skin Lesions Detection |
| title_short | An Evaluation of Image Preprocessing in Skin Lesions Detection |
| title_sort | An Evaluation of Image Preprocessing in Skin Lesions Detection |
| topic | Skin Lesion Classification Convolutional Neural Networks Deep Learning Image Preprocessing |
| topic_facet | Skin Lesion Classification Convolutional Neural Networks Deep Learning Image Preprocessing |
| url | http://hdl.handle.net/10198/30386 |
| visible | 1 |