Detection of tumor tissue based on the multispectral imaging

Authors:
Adam Świtoński;Marcin Michalak;Henryk Josiński;Konrad Wojciechowski
Affiliations:
Polish-Japanese Institute of Information Technology, Bytom, Poland and Silesian University of Technology, Gliwice, Poland;Silesian University of Technology, Gliwice, Poland and Central Mining Institute, Katowice, Poland;Polish-Japanese Institute of Information Technology, Bytom, Poland and Silesian University of Technology, Gliwice, Poland;Polish-Japanese Institute of Information Technology, Bytom, Poland and Silesian University of Technology, Gliwice, Poland
Venue:
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Year:
2010

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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.