Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/71341

TítuloA deep learning line to assess patient’s lung cancer stages
Autor(es)Dias, André
Fernandes, João Vieira
Monteiro, Rui
Machado, Joana
Ferraz, Filipa Tinoco
Neves, João
Sampaio, Luzia
Ribeiro, Jorge
Vicente, Henrique
Alves, Victor
Neves, José
Palavras-chaveCase-based reasoning
Computed Tomography
Intelligent systems
Knowledge representation and reasoning
Logic programming
Lung cancer
Data2019
EditoraSpringer Verlag
RevistaAdvances in Intelligent Systems and Computing
CitaçãoDias A. et al. (2019) A Deep Learning Line to Assess Patient’s Lung Cancer Stages. In: Yang XS., Sherratt S., Dey N., Joshi A. (eds) Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 797. Springer, Singapore. https://doi.org/10.1007/978-981-13-1165-9_55
Resumo(s)Our goal is to pursue a vision of developing and maintaining a comprehensive and integrated computer model to help physicians plan the most appropriate treatment and anticipate a patient’s prospects for the extent of cancer. For example, cancer can be treated at an early stage by surgery or radiation, while chemotherapy may be the care for more advanced stages. In fact, early detection of this type of cancer facilitates its treatment and may rise the patients’ prospect of a continued existence. Thus, a formal view of an intelligent system for performing cancer feature extraction and analysis in order to establish the bases that will help physicians plan treatment and predict patient’s prognosis is presented. It is based on the Logic Programming Language and draws a line between Deep Learning and Knowledge Representation and Reasoning, and is supported by a Case Based attitude to computing. In fact, despite the fact that each patient’s condition is different, treating cancer at the same stage is often similar.
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/71341
ISBN978-981-13-1164-2
e-ISBN978-981-13-1165-9
DOI10.1007/978-981-13-1165-9_55
ISSN2194-5357
Versão da editorahttps://link.springer.com/chapter/10.1007%2F978-981-13-1165-9_55
Arbitragem científicayes
AcessoAcesso restrito UMinho
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

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