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

  • André Dias
  • João Fernandes
  • Rui Monteiro
  • Joana Machado
  • Filipa Ferraz
  • João Neves
  • Luzia Sampaio
  • Jorge Ribeiro
  • Henrique VicenteEmail author
  • Victor Alves
  • José NevesEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 797)


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.


Logic programming Knowledge representation and reasoning Intelligent systems Case-based reasoning Lung cancer Computed Tomography 



This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Departamento de Informática, Escola de EngenhariaUniversidade Do MinhoBragaPortugal
  2. 2.Farmácia de LamaçãesBragaPortugal
  3. 3.Centro AlgoritmiUniversidade Do MinhoBragaPortugal
  4. 4.Mediclinic Arabian RanchesDubaiUnited Arab Emirates
  5. 5.Dubai Healthcare CityDubaiUAE
  6. 6.Escola Superior de Tecnologia E Gestão, ARC4DigiT—Applied Research Center for Digital Transformation Instituto Politécnico de Viana Do CasteloViana Do CasteloPortugal
  7. 7.Departamento de Química, Escola de Ciências E Tecnologia, Centro de Química de ÉvoraUniversidade de ÉvoraÉvoraPortugal

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