Skin malignancy - an anomalous development of skin cells - frequently creates on uncovered skin. This basic type of malignant growth ordinarily happens on the skin without exposure of sunlight. There are three primary sorts of skin malignancy - basal cell carcinoma, squamous cell carcinoma and melanoma. It can be minimized the risk of skin disease by restricting or forestalling ultraviolet (UV) radiation. Testing the skin for dubious changes can help identify skin malignancy at a beginning phase. Automatic detection of skin cancer is a very effective tool, especially in the absence of specialists. Image processing has been practiced in various fields over the past decades, allowing to improve the interpretation, representation, and processing information of an image. Here the proposed system is based on Grey Level Co-occurrence Matrices (GLCM) and Support Vector Machine (SVM). A GLCM is a histogram of co-occurring greyscale values at a given counterbalance over an image. SVM kernel method has been used to classify the skin lesion and identify the type of skin cancer. System achieved 96.36 % of accuracy with minimal false alarm rate.

Skin Cancer, GLCM, Basal Cell Carcinoma, Squamous Cell Carcinoma, Melanoma, Segmentation, Support Vector Machine

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