A Decision Support System Based on the Semantic Analysis of Melanoma Images Using Multi-elitist PSO and SVM

  • Weronika Pia̧tkowska
  • Jerzy Martyna
  • Leszek Nowak
  • Karol Przystalski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6871)

Abstract

The use of machine learning tools for the purpose of medical diagnosis is gradually increasing. This is mainly because the effectiveness of classification has improved a great deal to help medical experts in diagnosing diseases. Such a disease is melanoma malignum, which is a very common type of cancer among humans. In this paper, we use modified version of classical Particle Swarm Optimization (PSO) algorithm, known as the Multi-Elitist PSO (MEPSO) method and support vector machines (SVM) to classify melanoma malignum images previously preprocessed by image segmentation and image feature extraction. The classification accuracy obtained is ca. 96%. The proposed classification method can be developed to an automatic classification process, the performance of which is similar to human perception.

Keywords

Support Vector Machine Particle Swarm Optimization Semantic Category Segmented Region Support Vector Machine Method 
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

  • Weronika Pia̧tkowska
    • 1
  • Jerzy Martyna
    • 2
  • Leszek Nowak
    • 1
  • Karol Przystalski
    • 1
  1. 1.Institute of Applied Computer ScienceJagiellonian UniversityCracowPoland
  2. 2.Institute of Computer ScienceJagiellonian UniversityCracowPoland

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