Detection of Tumor Tissue Based on the Multispectral Imaging

  • Adam Świtoński
  • Marcin Michalak
  • Henryk Josiński
  • Konrad Wojciechowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)

Abstract

We have prepared multispectral image database of skin tumor diagnosis. All images have been labeled with two classes - tumor and healthy tissues. We have extracted pixel signatures with their spectral data and class assigning, thus obtained train dataset. Next we have used and evaluated the supervised learning techniques for the purpose of automatic tumor detection. We have tested Naive Bayes, KNN, Multilayer Perceptron, LibSVM, LibLinear, RBFNetwork, ConjuctiveRule, DecisionTable and PART classifiers. We have obtained results on the level of 99% classifier efficiency. We have visualized classification for example images by coloring class regions and verified if they overlap with labeled regions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adam Świtoński
    • 1
    • 2
  • Marcin Michalak
    • 2
    • 3
  • Henryk Josiński
    • 1
    • 2
  • Konrad Wojciechowski
    • 1
    • 2
  1. 1.Polish-Japanese Institute of Information TechnologyBytomPoland
  2. 2.Silesian University of TechnologyGliwicePoland
  3. 3.Central Mining InstituteKatowicePoland

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