Genetic programming for skin cancer detection in dermoscopic images

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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