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The incidence of malignant melanoma continues to increase worldwide. This can strike at any age; it is one of the leading causes of loss of life young persons. Since this cancer is visible on the skin, it is potentially at a very early stage when it is curable. New developments have to make fully automatic early melanoma detection a real possibility. , the advent of dermoscopy has enabled a dramatic boost in clinical ability to the point that melanoma can be detected in the clinic at very earliest stages. The global adoption of this technology has allowed of large collections of dermoscopy images of melanomas and benign validated by histopathology. The development of advanced technologies the areas of image processing and machine learning have given us the ability allow distinction of malignant melanoma from the many benign mimics that no biopsy. These new technologies should allow not only earlier of melanoma, but also reduction of the large number of needless and biopsy procedures. Although some of the new systems reported for these have shown promise in preliminary trials, widespread must await further technical progress in accuracy and. In this paper, we provide an overview of computerized of melanoma in dermoscopy images. First, we discuss the various of lesion segmentation. Then, we provide a brief overview of clinical segmentation. Finally, we discuss the classification stage where learning algorithms are applied to the attributes generated from the features to predict the existence of melanoma.
Mishra et al. (Thu,) studied this question.