Enhanced K-mean Using Evolutionary Algorithms for Melanoma Detection and Segmentation in Skin Images

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%.

@article{Aljawawdeh2017,
  title     = {Enhanced K-mean Using Evolutionary Algorithms for Melanoma Detection and Segmentation in Skin Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {12},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Asmaa Aljawawdeh and Esraa Imraiziq and Ayat Aljawawdeh},
  doi       = {10.14569/IJACSA.2017.081263},
  url       = {https://doi.org/10.14569/IJACSA.2017.081263}
}