Abstract
The incidence of skin cancer, particularly, malignant melanoma, continues to increase worldwide. If such a cancer is not treated at an early stage, it can be fatal. A computer system based on image processing and computer vision techniques, having good diagnostic ability, can provide a quantitative evaluation of these skin cancer cites called skin lesions. The size of a medical image is usually large and therefore requires reduction in dimensionality before being processed by a classification algorithm. Feature selection and construction are effective techniques in reducing the dimensionality while improving classification performance. This work develops a novel genetic programming (GP) based two-stage approach to feature selection and feature construction for skin cancer image classification. Local binary pattern is used to extract gray and colour features from the dermoscopy images. The results of our proposed method have shown that the GP selected and constructed features have promising ability to improve the performance of commonly used classification algorithms. In comparison with using the full set of available features, the GP selected and constructed features have shown significantly better or comparable performance in most cases. Furthermore, the analysis of the evolved feature sets demonstrates the insights of skin cancer properties and validates the feature selection ability of GP to distinguish between benign and malignant cancer images.
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Ain, Q.U., Xue, B., Al-Sahaf, H., Zhang, M. (2018). Genetic Programming for Feature Selection and Feature Construction in Skin Cancer Image Classification. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_56
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