Skin Lesion Analysis Towards Melanoma Detection

Melanoma is one of the most lethal forms of skin cancer. The overall aim of the proposed skin lesion analysis system is to fortify efforts to reduce melanoma cognate deaths and dispensable biopsies by ameliorating the precision and efficiency of early melanoma detection. Image analysis implements enable the automated diagnosis of melanoma from dermoscopic images. Skin lesion analysis is composed of mainly four stages like lesion segmentation, preprocessing, dermoscopic feature extraction and disease classification. This paper proposes methods and algorithms to perform lesion analysis. The proposed solution utilizes the combination of K-Means clustering algorithm and thresholding for lesion segmentation, sobel operator and median filter for pre-processing, GLCM and LBP features for feature extraction and SVM classifier for classification. The framework is evaluated on ISIC Archive dataset. The proposed system is efficient and achieves an accuracy of 90%.