期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2014
卷号：3
期号：3
页码：10014
出版社：S&S Publications
摘要：In this paper we used hybrid spatial features representation and Radial basis type network classifier toclassify melanoma skin lesion. There are five different skin lesions commonly grouped as Actinic Keratosis, Basal CellCarcinoma, Melanocytic Nevus / Mole, Squamous Cell Carcinoma, Seborrhoeic Keratosis. To classify the queriedimages automatically and to decide the stages of abnormality, the automatic classifier PNN with RBF will be used, thisapproach based on learning with some training samples of each stage. Here, the color features from HSV space anddiscriminate texture features such as gradient, contrast, kurtosis and skewness are extracted. The lesion diagnosticsystem involves two stages of process such as training and classification. An artificial neural network Radial basistypes is used as classifier. The accuracy of the proposed neural scheme is high among five common classes of skinlesions .This will give the most extensive result on non-melanoma skin cancer classification from color imagesacquired by a standard camera (non-ceroscopy). Final experimental result shows that the texture descriptors andclassifier yields the better classification accuracy in all skin lesion stages.
