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eFis: A Fuzzy Inference Method for Predicting Malignancy of Small Pulmonary Nodules

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Image Analysis and Recognition (ICIAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8815))

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Abstract

Predicting malignancy of small pulmonary nodules from computer tomography scans is a difficult and important problem to diagnose lung cancer. This paper presents a rule based fuzzy inference method for predicting malignancy rating of small pulmonary nodules. We use the nodule characteristics provided by Lung Image Database Consortium dataset to determine malignancy rating. The proposed fuzzy inference method uses outputs of ensemble classifiers and rules from radiologist agreements on the nodules. The results are evaluated over classification accuracy performance and compared with single classifier methods. We observed that the preliminary results are very promising and system is open to development.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Hacettepe University, Ankara, Turkey

    Aydın Kaya & Ahmet Burak Can

Authors
  1. Aydın Kaya
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  2. Ahmet Burak Can
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Corresponding author

Correspondence to Aydın Kaya .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Dept. of Electrical and Computer Eng., University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

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© 2014 Springer International Publishing Switzerland

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Kaya, A., Can, A.B. (2014). eFis: A Fuzzy Inference Method for Predicting Malignancy of Small Pulmonary Nodules. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-11755-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11754-6

  • Online ISBN: 978-3-319-11755-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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