## 4 Results
### Machine-Learning Methods
In [78], shape (normalised radial length (4 features), asymmetry of shape (2 features)), color (statistical color measures (12 features), six-color model (7 features)), and texture (statistical texture measures (8 features), energy of Laws' filters responses (14 features), gray-level co-occurrence matrix features (24 features)) are extracted from all areas of the lesion (general features), as well as some texture features from peripheral regions only (local features). The sequential feature selection (SFS) approach is used and then classification is performed by using two different models: on the one hand a linear SVM model to recognise melanoma versus nevus on the basis of four lesion features, and on the other hand the RUSBoost classifier to recognise melanoma versus nevus and atypical nevus on the basis of eight features considered relevant by the SFS algorithm. In [79], an original and innovative system for automatic melanoma skin detection (ASMD) with melanoma index (MI) is proposed. The system incorporates image preprocessing, bi-dimensional empirical mode decomposition (BEMD), image texture enhancement, entropy and energy feature extraction and binary classification. From the feature-extraction stage, vectors of 28 features are obtained for each image, and a Student's \(t\)-test with triple cross-validation is used to classify the 28 features based on the statistical results obtained. In the classification phase, the combination SVM and radial basis function (RBF) offers high accuracy, which prompts the authors of the paper to formulate a clinically relevant MI based on Renyi entropy and maximum entropy. The MI value can help dermatologists decide whether a suspected skin lesion, shown in dermoscopic images, is benign or malignant. In [80], an integrated computer-aided method for multiclass classification of melanoma, dysplastic nevi, and basal cell carcinoma is proposed. Different features related to shape, edge irregularity, color and texture (obtained by combining GLCM and a fractal-based regional texture analysis (FRTA)) of skin lesions are extracted. Finally, the combination of feature selection with recursive feature elimination method (RFE) and a SVM with RBF function is used to perform classification. The paper [81] addresses the problem of amorphous pigmentary lesions and blurred edges by proposing two new fractal signatures called \(S_{STF}\) statistical fractal signatures and \(S_{SPF}\) statistical prism-based fractal signatures. The comparison of different computer-aided diagnosis methods for multiclass skin lesion classification based on the new fractal signatures, and using different classifiers, is performed. The best results for robust, unbiased, and reproducible methodologies are obtained by using \(S_{STF}\) with the LDA classifier. In [82], the authors suggest a skin lesion segmentation and classification system based on sparse kernel representation. They use a kernel dictionary and classifier to predict the labels of the test set. In particular, they first extract the texture features (speeded up robust features, or SURF) from the images, then, by using the KOMP algorithm, compute the sparse code of it with respect to the kernel dictionary, and finally, the classifier is used to predict the class of the lesion. They perform both binary classification (melanoma/normal) and multiclass classification (melanoma, basal cell carcinoma, and nevi). The authors of [83] propose a methodology for the accurate diagnosis of melanoma from dermoscopic images that consists of extracting and selecting salient features from the preprocessed and segmented images and classifying them by using multilayer perceptron (MPL)-averaged. Both the feature extraction and classification steps are optimized by a newly developed version of the red fox optimization (DRFO) algorithm.