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A Multi-stage Classification Method in Application to Diagnosis of Larynx Cancer

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 3070)

Abstract

In this paper, a multi-stage classification method is applied to a problem of larynx cancer diagnosis. The biochemical tumor markers, called CEA and SCC, as well as ferritin, and other factors, are used in order to produce the diagnosis. A neuro-fuzzy network is employed at every stage of the classification method. The networks reflect fuzzy IF-THEN rules, formulated based on the data containing measurements of the particular factors (attributes). The classification method is proposed to support a medical doctor decision, and provide additional useful information concerning the diagnosis.

Keywords

  • Membership Function
  • Beta Globulin
  • Data Item
  • Data Vector
  • Global Protein

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2004 Springer-Verlag Berlin Heidelberg

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Rutkowska, D., Klimala, J.K. (2004). A Multi-stage Classification Method in Application to Diagnosis of Larynx Cancer. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_162

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_162

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

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