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

Weronika Piatkowska, Jerzy Martyna, Leszek Nowak, Karol Przystalski

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.