A skin cancer classification approach using GAN and ROI-based attention mechanism

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorTeodoro, Arthur A. M.-
Autor(es): dc.creatorSilva, Douglas H.-
Autor(es): dc.creatorRosa, Renata L.-
Autor(es): dc.creatorSaad, Muhammad-
Autor(es): dc.creatorWuttisittikulkij, Lunchakorn-
Autor(es): dc.creatorMumtaz, Rao Asad-
Autor(es): dc.creatorRodríguez, Demóstenes Z.-
Data de aceite: dc.date.accessioned2026-02-09T12:55:09Z-
Data de disponibilização: dc.date.available2026-02-09T12:55:09Z-
Data de envio: dc.date.issued2022-10-24-
Data de envio: dc.date.issued2022-10-24-
Data de envio: dc.date.issued2022-04-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/55337-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s11265-022-01757-4-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1170903-
Descrição: dc.descriptionSkin cancer is a complex public health problem and one of the most common types of cancer worldwide. A biopsy of the skin lesion gives the definitive diagnosis of skin cancer. However, before the definitive diagnosis, specialists observe some symptoms that justify the request for a biopsy and consider a early diagnosis. Early diagnosis of skin cancer is subject to errors due to the lack of experience of specialists and similar characteristics with other diseases. This work proposes a CNN architecture, called EfficientAttentionNet, to provide early diagnosis of melanoma and non-melanoma skin lesions. The methodology represents the stages of development of the proposed classification model and the benefits of each stage. In the first step, the set of images from the International Society for Digital Skin Imaging (ISDIS) is pre-processed to eliminate the hair around the skin lesion. Then, a Generative Adversarial Networks (GAN) model generates synthetic images to balance the number of samples per class in the training set. In addition, a U-net model creates masks for regions of interest in the images. Finally, EfficientAttentionNet training with the mask-based attention mechanism to classify skin lesions. The proposed model achieved high performance, being a reference for future research in the classification of skin lesions.-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer Nature-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceJournal of Signal Processing Systems-
Palavras-chave: dc.subjectSkin cancer-
Palavras-chave: dc.subjectGenerative adversarial networks-
Palavras-chave: dc.subjectImage segmentation-
Palavras-chave: dc.subjectRoI-based attention mechanism-
Palavras-chave: dc.subjectCâncer de pele-
Palavras-chave: dc.subjectRedes adversárias generativas-
Palavras-chave: dc.subjectSegmentação de imagem-
Título: dc.titleA skin cancer classification approach using GAN and ROI-based attention mechanism-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

Não existem arquivos associados a este item.