The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification

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Conference Proceeding
Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011, 2011, pp. 680 - 685
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This paper proposes an automatic skin cancer (melanoma) classification system. The input for the prosed system is a collected data images, it followed by different image processing procedures to enhance the image properties. Two segmentation methods used to identify the normal skin cancer from malignant skin and to extract the useful information from these images that passed to the classifier for training and testing. The features used for classification is the coefficients created by Wavelet decompositions and simple wrapper curvelet. Curvelet is suitable for the image that contains oriented texture and cartoon edges. Recognition accuracy of the three layers back-propagation neural network classifier with wavelet is 51.1% and with curvelet is 75. 6% in digital images database. © 2011 IEEE.
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