In recent days, skin cancer is one of the most dangerous form of the cancers found in humans. Skin cancer is found in various types such as Melanoma, Basal and Squamous Cells Carcinoma among which Melanoma is the most unpredictable. The diagnosis of Melanoma cancer in early stage will be helpful to cure it. Melanoma is type of skin cancer that evolve from melanocytic cells. Because of Malignancy feature melanoma skin cancer is also defined as Malignant Melanoma. Melanoma cancers have so many stages which will increase the death rate of patients. So early diagnosis and treatment of Melanoma implicate higher chances of cure. Traditional methods to diagnose skin cancer are excruciating, invasive and time consuming. So to overcome this problem different techniques used for skin cancer detection. These techniques use Machine learning and image processing tools for the detection of Melanoma skin cancer. The input to the system is the skin lesion image and then by applying image processing techniques, it analyses to conclude about the presence of skin cancer. The lesion image analysis tools checks for various Melanoma parameters which are like Asymmetry, Border, Colour and Diameter (ABCD) by texture, size and shape analysis for image segmentation and feature stages. The extricated feature parameters are used to classify the image as Normal skin and Melanoma cancer lesion.

Melanoma, Image processing, Classification, Machine Learning

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