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Title: A Deep Learning Line to Assess Patient’s Lung Cancer Stages
Authors: Dias, André
Fernandes, João
Monteiro, Rui
Machado, Joana
Ferraz, Filipa
Neves, João
Sampaio, Luzia
Ribeiro, Jorge
Vicente, Henrique
Alves, Victor
Neves, José
Keywords: Logic Programming
Knowledge Representation and Reasoning
Intelligent Systems
Case Based Reasoning
Lung Cancer
Computed Tomography
Issue Date: 2019
Publisher: Springer
Citation: Dias, A., Fernandes, J., Monteiro, R., Machado, J., Ferraz, F., Neves, J., Sampaio, L., Ribeiro, J., Vicente, H., Alves, V. & Neves, J., A Deep Learning Line to Assess Patient’s Lung Cancer Stages. Advances in Intelligent Systems and Computing, 797, 599–607, 2019.
Abstract: 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.
URI: 2194-5357 (paper)
ISSN: 2194-5365 (electronic)
Type: article
Appears in Collections:CQE - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
QUI - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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