## 8 Comparison with Other Methodologies
To handle the classes' unbalances, they repeated the melanoma features. The global method showed sensitivity = 96% and specificity = 80%. The local method presented sensitivity = 100% and specificity = 75%. [PERSON] et al. [40] used linear classifiers and a binary strategy. The dataset employed had 968 dermoscopy images. The detection rate reached was 90.48% for MEL, 82.51% for NEV, 82.61% for BCC, and 80.61% for seborrheic keratosis (SK). They employed 10-fold cross-validation to report the performance. [PERSON] et al. [41] proposed a gray-level difference method in conjunction with the ABCD features. The dataset had 300 dermoscopy images per class of MEL, NEV, BCC, pigmented benign keratosis (PBK). Operating with the SVM classifier and five-fold cross-validation they reports an accuracy = 100%, sensitivity = 100%, specificity = 100%, precision = 100%. [PERSON] et al. [37] used the grey level co-occurrence matrix (GLCM), local binary pattern (LBP), and color features to produced a binary classifier. The data set had 146 MEL and 251 NEV digital images. The classifiers used were SVM, KNN, Naive Bayes, and decision trees. They reported accuracy = 96%, sensitivity = 97%, specificity = 96%, and precision = 97%. [PERSON] [38] yielded a CNN to classify dermoscopy images of melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, basal cell carcinoma, and lentigo. He divided the dataset into three equal parts of 8000 images of benign and malignant categories. A total of 5600 images pertained to the training set and 2400 for the validation set. He obtained AUC = 0.98, accuracy = 97.49%, specificity = 93.6%, and sensitivity = 94.3%. [PERSON] et al. [39] gave a review of the state-of-the-art in the automated detection of nonmelanoma skin cancer. They reported the computer-aided diagnoses for dermoscopy and non-demoscopy images. [PERSON] et al. [42] trained five CNNs with SK, AK, rosacea (ROS), lupus erythematosus (LE), BCC, and SCC. They reported the best methodology results of 92.9%, 89.2%, and 84.3% recalls for the LE, BCC, and SK, respectively, and the mean recall and precision reached 77.0% and 70.8%. The training image set had: 1075 SK, 219 AK, 263 ROS, 1188 LE, 623 BCC, and 638 SCC. The test images set was composed of 52 SK, 58 AK, 55 ROS, 85 LE, 66 BCC, and 72 SCC. Different weights were used in the cost function to address the problem of data imbalance. [PERSON] et al. [44] proposed an ensemble of deep learning models to classify the dermoscopy color-images in the ISIC archive-2019 challenge. The methodology worked with cropped and binarized images. In addition, to avoid the imbalance of the classes, they made data augmentation. Here was reported specificity of 72.5%, and when the metadata were also used, the specificity increased to 74.2%. [PERSON] et al. [45] presented a computer-assisted diagnosis of the set of four classifiers that reports the average of the predictions. They used DermNet and the ISIC-2018 file. For the training step, the images were cropping, horizontal flipping, and resizing (as required by neural networks, for example, the size of images must be 224 \(\times\) 224 pixels or 331 \(\times\) 331 pixels). They used stratified \(k\)-fold cross-validation with \(k=5\). For the ISIC archive-2018 consisting of seven classes with a total of 23665 images, they obtained a weighted average for \(precision=85.02\pm 09.10\%\), \(sensitivity=80.46\pm 09.38\%\), \(specificity=96.57\pm 07.15\%\) and \(F1-Score=82.45\pm 08.38\) when classifying the 23,665 images.