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

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.

I. INTRODUCTION

Australia has one of the highest skin cancer rates in the world at nearly four times the rates in Canada, the US and the UK. it has been estimated 115,000 new cases of cancer diagnosed and more than 43,000 people are expected to die from cancer according to Cancer council of Australian 2010 [1], The Chair of Public Health Committee pronounced that, more than 430,000 cases treated for non-melanoma, and more than 10,300 people are treated for melanoma, with 1430 people dying each year [1].