Detection of Tumor Tissue Based on the Multispectral Imaging
- Cite this paper as:
- Świtoński A., Michalak M., Josiński H., Wojciechowski K. (2010) Detection of Tumor Tissue Based on the Multispectral Imaging. In: Bolc L., Tadeusiewicz R., Chmielewski L.J., Wojciechowski K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6375. Springer, Berlin, Heidelberg
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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