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 Network (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 |
| Outros Autores: | Lazzaretti, André; Monteiro, Fernando C. |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867173418684645376 |
|---|---|
| author | Silva, Giuliana |
| author2 | Lazzaretti, André Monteiro, Fernando C. |
| author2_role | author author |
| author_facet | Silva, Giuliana Lazzaretti, André Monteiro, Fernando C. |
| author_role | author |
| contributor_name_str_mv | . Biblioteca Digital do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Silva, Giuliana\"},{\"Person.name\":\"Lazzaretti, André\"},{\"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 Lazzaretti, André Monteiro, Fernando C. |
| datacite.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2026-05-18T13:42:45Z |
| datacite.date.embargoed.fl_str_mv | 2026-05-18T13:42:45Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| 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 Lazzaretti, André Monteiro, Fernando C. |
| dc.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2026-05-18T13:42:45Z |
| dc.date.embargoed.fl_str_mv | 2026-05-18T13:42:45Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/36707 |
| dc.language.none.fl_str_mv | eng |
| 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_abf2 |
| 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 Network (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 | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/dface39e-06bb-4ed5-bbc8-5d86379da82b/download |
| id | ipb_da448db909bf89ddeac21497d4e9da14 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/36707 |
| 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:10198/36707 |
| organization_str_mv | urn:organizationAcronym:ipb |
| person_str_mv | Silva, Giuliana Lazzaretti, André 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 | 2023 |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engengThis study aims to evaluate the impact of image preprocessing techniques on the performance of Convolutional Neural Network (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/pdfengAn evaluation of image preprocessing in skin lesions detectionSilva, GiulianaLazzaretti, AndréPersonalMonteiro, 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.com8986162600.HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-972-745-326-92026-05-18T13:42:45Z20232023-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/36707http://purl.org/coar/access_right/c_abf2open access381271 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2023http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/dface39e-06bb-4ed5-bbc8-5d86379da82b/downloadOL2A 20235757Ponta Delgada, Portugal2023 |
| spellingShingle | An evaluation of image preprocessing in skin lesions detection Silva, Giuliana |
| status | SINGLETON |
| 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 |
| url | http://hdl.handle.net/10198/36707 |
| visible | 1 |