Malignant melanoma is the deadliest form of skin cancer. Researches are attempting for the early automatic diagnosis of Melanoma, a lethal form of skin cancer, from dermoscopic images. The process includes different stages like pre-processing, lesion segmentation, dermoscopic feature detection within a lesion, feature extraction and disease classification. In this paper, we review the state-of-the-art computer aided diagnosis system for melanoma detection and examine recent practices in different steps of these systems. Statistics and results from the most important and recent implementations are analyzed and reported. We compared the performance of recent works based on different parameters like accuracy, sensitivity, specificity, machine learning techniques, dataset etc. Research challenges regarding the different parts of computer aided skin cancer diagnosis systems are also highlighted in this paper.

Skin cancer, Melanoma, Dermoscopy, Preprocessing, Image segmentation, Feature extraction, Classification, Ridgelet, K-Means, GLCM, SVM

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