Melanoma Diagnosis by the Use of Wavelet Analysis based on Morphological Operators

Abstract

Abstract—Skin melanoma is the most dangerous type of skin cancer which is curable if diagnosed at the right time. Drawing distinction between melanoma and mole is a difficult task and needs detailed laboratory tests. Utilizing morphologic operators in segmenting and wavelet analysis in order to extract the features has culminated in better result in melanoma diagnosis. This paper employs coefficients of wavelet decomposition to extract image’s features. Melanoma classification is carried out by using the variance and mean of wavelet coefficients of images as the inputs of neural network. Results show 90% ability in distinction between benign and malignant lesions.