文章基本信息

标题：Enhanced K-mean Using Evolutionary Algorithms for Melanoma Detection and Segmentation in Skin Images
本地全文：下载
作者：Asmaa Aljawawdeh ; Esraa Imraiziq ; Ayat Aljawawdeh 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：12
DOI：10.14569/IJACSA.2017.081263
出版社：Science and Information Society (SAI)
摘要：Nowadays, Melanoma has become one of the most significant public health concerns. Malignant Melanoma (MM) is considered the most rapidly spreading type of skin cancer. In this paper, we have built models for detection, segmentation, and classification of Melanoma in skin images using evolutionary algorithms. The first step was to enhance the K-mean algorithm by using two kinds of Evolutionary Algorithms: a Genetic Algorithm and the Particle Swarm Algorithm. Then the Enhanced Algorithms and the default k-mean separately were used to do detection and segmentation of skin cancer images. Then a feature extraction step was applied on the segmented images. Finally, the classification step was done by using two predictive models. The first model was built using a Neural Network backpropagation and the other one using some threshold values for some selected features. The results showed a high accuracy using Neural Back-propagation for the Enhanced K-mean by using a Genetic Algorithm, which achieved 87.5%.
关键词：Melanoma; genetic algorithm; K-mean; particle swarm optimization; classification; segmentation