A decision support system based on the semantic analysis of melanoma images using multi-elitist PSO and SVM

Authors:
Weronika Piatkowska;Jerzy Martyna;Leszek Nowak;Karol Przystalski
Affiliations:
Institute of Applied Computer Science, Jagiellonian University, Cracow, Poland;Institute of Computer Science, Jagiellonian University, Cracow, Poland;Institute of Applied Computer Science, Jagiellonian University, Cracow, Poland;Institute of Applied Computer Science, Jagiellonian University, Cracow, Poland
Venue:
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Year:
2011

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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.