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Research Article Open Access
In this paper we used hybrid spatial features representation and Radial basis type network classifier to classify melanoma skin lesion. There are five different skin lesions commonly grouped as Actinic Keratosis, Basal Cell Carcinoma, Melanocytic Nevus / Mole, Squamous Cell Carcinoma, Seborrhoeic Keratosis. To classify the queried images automatically and to decide the stages of abnormality, the automatic classifier PNN with RBF will be used, this approach based on learning with some training samples of each stage. Here, the color features from HSV space and discriminate texture features such as gradient, contrast, kurtosis and skewness are extracted. The lesion diagnostic system involves two stages of process such as training and classification. An artificial neural network Radial basis types is used as classifier. The accuracy of the proposed neural scheme is high among five common classes of skin lesions .This will give the most extensive result on non-melanoma skin cancer classification from color images acquired by a standard camera (non-ceroscopy). Final experimental result shows that the texture descriptors and classifier yields the better classification accuracy in all skin lesion stages.
Computer Aided Diagnosis , Texture Analysis , Skin Cancer , Neural Network , Segmentation, Skin Transplant