Applications of Neural Networks in Semantic Analysis of Skin Cancer Images

  • K. Przystalski
  • L. Nowak
  • M. Ogorzałek
  • G. Surówka
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 99)


Computational intelligence is finding more and more applications in computer aided diagnostics, helping doctors to process large quantities of various medical data [Buronni et al. 2004]. In dermatology it is extremely difficult to perform automatic diagnostic differentiation of malignant melanoma based only on dermatoscopic images. Applying artificial intelligence algorithms to explore and search large database of dermatoscopic images allow doctors to semantically filter out image with specified characteristics. This paper presents an semantic approach for characteristic objects classification found in image database of pigment skin lesions, based on radial basis function kernel for artificial neural networks. Presented approach is divided into few parts: JSEG image segmentation [Deng et al. 2001], feature extraction and classification. Prepared features vector consist of color models parts. For classification Artificial Neural Networks and Support Vector Machines are used and their performance is evaluated and compared. Success rates in both cases are greater than 90%.


Support Vector Machine Radial Basis Function Neural Network Radial Basis Function Radial Basis Function Kernel Object Extraction 
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 2012

Authors and Affiliations

  • K. Przystalski
    • 1
  • L. Nowak
    • 1
  • M. Ogorzałek
    • 1
  • G. Surówka
    • 1
  1. 1.Department of Information Technologies, Faculty of Physics, Astronomy and Applied Computer ScienceJagiellonian UniversityKrakowPoland

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