Genetic programming for skin cancer detection in dermoscopic images

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{ain:2017:CEC,
  author =       "Qurrat Ul Ain and Bing Xue and Harith Al-Sahaf and 
                 Mengjie Zhang",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Genetic programming for skin cancer detection in
                 dermoscopic images",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "2420--2427",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Development of an effective skin cancer detection
                 system can greatly assist the dermatologist while
                 significantly increasing the survival rate of the
                 patient. To deal with melanoma detection, knowledge of
                 dermatology can be combined with computer vision
                 techniques to evolve better solutions. Image
                 classification can significantly help in diagnosing the
                 disease by accurately identifying the morphological
                 structures of skin lesions responsible for developing
                 cancer. Genetic Programming (GP), an emerging
                 Evolutionary Computation technique, has the potential
                 to evolve better solutions for image classification
                 problems compared to many existing methods. In this
                 paper, GP has been used to automatically evolve a
                 classifier for skin cancer detection and also analysed
                 GP as a feature selection method. For combining
                 knowledge of dermatology and computer vision
                 techniques, GP has been given domain specific features
                 provided by the dermatologists as well as Local Binary
                 Pattern features extracted from the dermoscopic images.
                 The results have shown that GP has significantly
                 outperformed or achieved comparable performance
                 compared to the existing methods for skin cancer
                 detection.",
  keywords =     "genetic algorithms, genetic programming, cancer,
                 computer vision, feature selection, image
                 classification, medical image processing, patient
                 diagnosis, GP, computer vision techniques, dermoscopic
                 images, disease diagnosis, domain specific features,
                 evolutionary computation technique, feature selection
                 method, local binary pattern features, melanoma
                 detection, patient survival rate, skin cancer
                 detection, Feature extraction, Image color analysis,
                 Malignant tumors, Mutual information, Sensitivity,
                 Skin, Skin cancer",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969598",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969598}",
}

Genetic Programming entries for Qurrat Ul Ain Bing Xue Harith Al-Sahaf Mengjie Zhang

Citations