###### Abstract
The segmentation step is crucial to increase the effectiveness of subsequent steps as clinically important features, such as blue-white areas, atypical pigmented networks and globules, can only be automatically extracted when the accuracy of lesion edge detection is high [28]. This is a crucial task that researchers need to perform to aim for the best results [7; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. The feature-extraction step can be either manual [39] or automated by means of machine-learning algorithms. The extraction of handcrafted features relevant in the case of skin lesion classification is based on the methodologies designed by dermatologists to perform skin cancer diagnosis, and, in particular, the ABCD rule of dermoscopy. The main operations used for the extraction of shape, color, and texture features of skin lesions are given below. * Shape: computation of area, perimeter, compactness index, rectangularity, bulkiness, major and minor axis length, convex hull, comparison with a circle, eccentricity, Hu's moment invariants, wavelet invariant moments, Zunic compactness, symmetry maps, symmetry distance, and adaptive fuzzy symmetry distance. * Color: computation of average, standard deviation, variance, skewness, maximum, minimum, entropy, 1D or 3D color histograms, and the autocorrelogram. In addition, several techniques have been used to group the pixels, namely k-means, Gaussian mixture model (GMM), and multi-thresholding. * Texture: computation of the gray-level co-occurrence matrix (GCLM), gray level run-length matrix (GLRLM), local binary patterns (LBP), wavelet and Fourier transforms, fractal dimension, multidimensional receptive fields histograms, Markov random fields, and Gabor filters. By using machine-learning methods, learned features are derived automatically from the datasets and require no prior knowledge of the problem. Even for the final classification phase, different approaches are possible, from the classical ones, to the cutting-edge  
Figure 2: CAD’s pipeline for skin lesion image analysis. methodologies based on deep convolutional neural networks. The techniques used to classify skin lesions are similar to those used for other types of cancer, such as breast, thyroid, colorectal, lung, pancreatic, and cervical cancers [40; 41; 42; 43; 44]. In this review article, various approaches are examined to then determine which show the best performance in the tasks of skin lesion classification and skin cancer detection. Many studies show that the performance of DL algorithms equals or even exceeds the performance of experienced dermatologists in detecting and diagnosing skin lesions [45; 46; 47; 48; 49; 50; 51; 52]. However, the performance of these algorithms should also be evaluated on images outside their area of expertise [45]. Several difficulties and challenges exist in the automatic classification of dermoscopic images using ML and DL methods [53], such as high variability in the shape, size, and location of lesions, the low contrast between skin lesions and surrounding healthy skin, the visual similarity between melanoma and nonmelanoma lesions, and the variation in skin condition among different patients. Regarding this last point, a very important but little addressed aspect is skin color [54]. In fact, the dermoscopic datasets used for training machine-learning models contain images of light-skinned people. To perform accurate detection of skin lesions in dark-skinned people, it is necessary to expand the existing datasets and fill this gap. Opportunities of the use of ML and DL methods for skin cancer detection, in addition to those already mentioned, include the possibility of avoiding unnecessary biopsies or missed melanomas, but also of making skin diagnoses without the need for physical contact with the patient's skin and reducing the cost of diagnosis and treatment of nonmelanoma skin cancer, which is found to be considerable [55]. The paper is organised into the following sections. * Section 2.