A Deep Learning Line to Assess Patient’s Lung Cancer Stages

• Autor(a) principal: Dias, André
• Data de Publicação: 2019
• Outros Autores: Fernandes, João, Monteiro, Rui, Machado, Joana, Ferraz, Filipa, Neves, João, Sampaio, Luzia, Ribeiro, Jorge, Vicente, Henrique, Alves, Victor, Neves, José
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/25883; https://doi.org/10.1007/978-981-13-1165-9_55
• topic: Logic Programming; Knowledge Representation and Reasoning; Intelligent Systems; Case Based Reasoning; Lung Cancer; Computed Tomography

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.