Study of Various Neural Networks to Improve the Defuzzification of Fuzzy Clustering Algorithms for ROIs Detection in Lung CTs

  • Alberto Rey
  • Alfonso Castro
  • Bernardino Arcay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6691)


The detection of pulmonary nodules in CT images has been extensively researched because it is a highly complicated and socially interesting matter. The classical approach consists in the development of a computer-aided diagnosis (CAD) system that indicates, in phases, the presence or absence of nodules. A common phase of these systems is the detection of regions of interest (ROIs), that may correspond to nodules, in order to reduce the searching space. This paper evaluates the use of various neural networks for the defuzzification of the output of fuzzy clustering algorithms, in order to improve the detection of true positives and the reduction of false positives. Also, they are compared to the results from a support vector machine (SVM).


Support Vector Machine False Positive Rate True Positive Rate Radial Basis Function Neural Network Fuzzy Cluster Algorithm 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alberto Rey
    • 1
  • Alfonso Castro
    • 1
  • Bernardino Arcay
    • 1
  1. 1.Faculty of Computer ScienceUniversity of A CoruñaSpain

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