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Automated Recognition of Malignant Melanoma in Dermoscopic Images
Abstract
This paper presents a novel method to detect and classify skin lesions of dermoscopic images for melanoma diagnosis with high accuracy. The proposed Computer Aided Diagnosis system first used unsupervised K-means clustering for segmentation process and lesion detection, and then it employed Discrete Wavelet Transform (DWT) and the GLCM matrix, to extract features from dermoscopic images. Finally, the classification step is assured by the machine learning method Kernel Support Vector Machines (KSVM).
The performance of the proposed method was evaluated using PH2 database images with a total of 200 dermoscopic images containing 80 atypical nevi, 80 common nevi, and 40 melanomas.
The proposed approach has achieved high levels of sensitivity, specificity, and accuracy (73.33%, 86.66%, and 82.22%, respectively) without consuming too much computation time.
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