Wavelet-based energy features for diagnosis of melanoma from dermoscopic images

Melanoma causes majority of deaths related to skin cancer if not detected and treated at an early stage. It is considered as one of the dangerous types of cancer, since it quickly spreads to other parts. A novel approach has been proposed in this paper to detect Melanoma from dermoscopic images. Pre-processing is done to remove hair and noise in the image. Initial segmentation is carried out with watershed transform. This is followed by Maximal Similarity Region Merging process. After pre-processing and segmentation, wavelet-based energy features are extracted using daubechies (DB3), and reverse biorthogonal (RBIO3.3, RBIO3.5, and RBIO3.7) wavelet filters. 12 features are extracted using four wavelet filters. Using the Gain Ratio feature selection method, the most discriminative six features are selected for the classification purpose. Then classification has been done by using K-nearest neighbour, support vector machine, random forest, and Naive Bayes classifier. The highest sensitivity of 97.5% is achie...