# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 2 Related Studies  
In recent years, many CAD systems have been developed for the classification of pigmented skin lesions. Some of them have been designed for the detection of global patterns, while others have been used for the detection of localized criteria. The former look for particular patterns of texture structures (pigmented network, globules, etc. ), while the latter are based on medical rules, such as ABCDE and the seven-point checklist [23]. None of the various methods in use can be considered optimal as they are as affected by the computational cost, not very high performance, and/or implementation complexity. For this reason, the recognition of skin lesions is still a challenging problem [24]. The challenge is made harder by the low contrast between the lesion and healthy skin, variegated colors, irregular borders, artifacts (such as bubbles or body hairs), and blood vessels [25]. To deal with these issues, in [26], a procedure of feature reduction adopting a higher variance precise entropy is proposed. After a preprocessing phase, a semi-automatic segmentation technique is used, which converts the preprocessed image into the HSV color space and then selects the V-channel as input for both a seeded region growing and a graph cut segmentation. The two segmented images are then fused pixel by pixel, and multilevel feature extraction is performed for lesion classification. Additionally, [PERSON] et al. [27] developed a method for melanoma detection based on a combination of a graph theory segmentation technique and L-type fuzzy number-based approximations for a more accurate selection of the cancer area. The ABCD rule has been used by some [28] to analyze the border and color of skin lesions after the adoption of a Gaussian filter and contour method for the region of interest extraction. Other authors [29] have implemented the adaptive thresholding technique for the segmentation phase and the ABCD scoring method to extract features, which are then classified using a Support Vector Machine. Other studies have approached skin lesions by looking for the presence of patterns. These methods attempt to detect global patterns based on three categories: texture, shape, and color [30]. [PERSON] et al. [31] also used texture descriptors based on statistical measurements. [PERSON] et al. [32] converted the image into gray levels and then adopted a local binary pattern and Haralick features as texture features to be extracted from the lesion being tested. A novel approach to extract color and texture features as a single feature--a multi-direction 3D color texture feature--was presented by [PERSON] et al. [33]. The subsequent detection of melanoma was obtained through a 15 hidden layers backpropagation neural network classifier. It is trained by employing the scaled conjugate gradient backpropagation algorithm. [PERSON] et al. [34] adopted a kernel sparse representation for the generation of discriminative sparse codes to represent features in a high-dimensional feature space. They used an adaptive K-Singular Value Decomposition algorithm for learning, segmentation, and classification methods that are undisturbed by image conditions. The combination of linearly independent and linear prediction algorithms has been used by some authors [35] to measure feature similarity, while in another paper [36], orientation histogram, gradient location, and color features are fused and used to differentiate between malignant and benign skin lesions, adopting a Support Vector Machine classifier. A deeply supervised encoder-decoder network connected using skip pathways is presented in [37]. This series of skip pathways is composed of convolutional networks and short skip networks. A new convolutional neural network is used for skin lesion segmentation and pixel-wise classification. Additionally, the authors in [38] implemented a convolutional neural network for efficient classification. The network was pre-trained using large AlexNet and ResNet50 transfer learning. [PERSON] et al.