A survey on Barrett's esophagus analysis using machine learning

Registro completo de metadados
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorde Souza, Luis A.-
Autor(es): dc.creatorPalm, Christoph-
Autor(es): dc.creatorMendel, Robert-
Autor(es): dc.creatorHook, Christian-
Autor(es): dc.creatorEbigbo, Alanna-
Autor(es): dc.creatorProbst, Andreas-
Autor(es): dc.creatorMessmann, Helmut-
Autor(es): dc.creatorWeber, Silke-
Autor(es): dc.creatorPapa, João P.-
Data de aceite: dc.date.accessioned2021-03-11T00:57:02Z-
Data de disponibilização: dc.date.available2021-03-11T00:57:02Z-
Data de envio: dc.date.issued2018-12-11-
Data de envio: dc.date.issued2018-12-11-
Data de envio: dc.date.issued2018-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.compbiomed.2018.03.014-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/179742-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/179742-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionCalifornia Department of Fish and Wildlife-
Descrição: dc.descriptionProcesso FAPESP: 2013/07375-0-
Descrição: dc.descriptionProcesso FAPESP: 2014/12236-1-
Descrição: dc.descriptionProcesso FAPESP: 2016/19403-6-
Descrição: dc.descriptionCNPq: 306166/2014-3-
Descrição: dc.descriptionCNPq: 307066/2017-7-
Descrição: dc.descriptionCalifornia Department of Fish and Wildlife: PA 1595/3-1-
Descrição: dc.descriptionThis work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods.-
Formato: dc.format203-213-
Idioma: dc.languageen-
Relação: dc.relationComputers in Biology and Medicine-
Relação: dc.relation0,591-
Direitos: dc.rightsopenAccess-
Palavras-chave: dc.subjectAdenocarcinoma-
Palavras-chave: dc.subjectBarrett's esophagus-
Palavras-chave: dc.subjectComputer-aided diagnosis-
Palavras-chave: dc.subjectImage processing-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectPattern recognition-
Título: dc.titleA survey on Barrett's esophagus analysis using machine learning-
Tipo de arquivo: dc.typevídeo-
Aparece nas coleções:Repositório Institucional - Unesp

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