Wavelet and Curvelet Analysis for Automatic Identification of Melanoma Based on Neural Network Classification

MIR Labs
Publication Type:
Journal Article
International Journal of Computer Information Systems and Industrial Management (IJCISIM), 2013, 5 (1), pp. 606 - 614
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This paper proposes an automatic skin cancer (melanoma) classification system. The input for the proposed system is a set of images for benign and malignant skin lesions. Different image processing procedures such as smoothing and equalization are applied on these images to enhance their properties. Two segmentation methods are then used to identify the skin lesions before extracting the useful feature information from these images. This information is then passed to the classifier for training and testing. The features used for classification are coefficients created by Wavelet decompositions or simple wrapper Curvelets. Curvelets are known to be more suitable for the images that contain oriented textures and cartoon edges. The recognition accuracy obtained by the two layers back-propagation neural network classifier tested in this experiment is 58.44 % for the Wavelet based coefficients and 86.57 % for the Curvelet based ones
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