LAUSR

Skin cells grow in an orderly and controlled manner, in which old cells are pushed to the skin surface by healthy new cells, wherein they finally perish. However, when several cells damage DNA, new ones may start growing disorderly and can finally create cancer cells mass. Different types of skin cancer have been observed by experts. Although, melanoma, as one of the rarely happened cancers, is the most threatening type. Therefore, early detection of this type of cancer can be so useful for avoiding melanoma dangers and even helps to treat this type of cancer. The present paper proposes a new hierarchical procedure for the optimum diagnosis of melanoma cancer from dermoscopy images. Here, after image preprocessing, image segmentation is applied for the segmentation of the ROI. Then, the selection of features from the segmented images is performed and injected to a radial basis function‐based classifier to provide the final diagnosis of cancer. To deliver efficient results, the feature selection and the classifier have been optimized by a new design of Horse herd optimization algorithm (HHOA). The method is then implemented on the Society for Imaging Informatics in Medicine‐The International Skin Imaging Collaboration (SIIM‐ISIC) Melanoma dataset to validate its effectiveness and its achievements and also put in comparison with several different latest techniques. The results show that the proposed method with 95.46% precision provides the maximum performance among the others.