## 4 Results
### ML/DL Hybrid Techniques  
For the task of identifying and classifying skin cancer in dermoscopic images, in [130] a hybrid-dense algorithm is proposed. This algorithm consists of the extraction of skin lesion features with the pre-trained DenseNet121 network and the subsequent dimensionality reduction of the obtained vectors. Finally, classification is performed with the XGBoost classifier. The developed algorithm shows robustness in testing, so it is designed as a viable alternative in the identification of cancer-like diseases in skin lesions. A mix of images, hand-extracted features, and metadata is used in [131] to perform a multiclass classification based on ensemble networks. Multiple multi-input single-output (MISO) models, obtained by replacing the backbones with EfficientNet networks B4 to B7, are trained with the images to extract features, whereas the hand-extracted features and metadata are used for training an MPL with two dense layers. The outputs of the networks are then sent to an ANN, consisting of two dense layers, which will perform the final classification. In [132], 200 geometric features are extracted from the images, which are then injected into the last convolutional layer of two pretrained DL architectures (ResNet50 and DenseNet201). Both models are then used as feature extractors, sent to an SVM model for final prediction. In [133], the efficiency of 17 pretrained CNNs used as feature extractors and 24 classifiers is examined. The best combinations are obtained by using DenseNet201 in combination with FineKNN or CubicSVM. The combination of hand-coded features, sparse coding methods, and SVM with recent ML techniques (deep residual network and fully CNN) is presented in [134]. The features, extracted by hand, by sparse coding methods and by neural networks, are finally sent to an SVM classifier. For skin lesion classification, the integration of handcrafted (HC) features (of texture, color, and shape) and features extracted by DL (using ResNet50V2 and EfficientNetB0) is also used in [135]. After obtaining vectors of the features (HC, from ResNet and EfficientNet), they train the ANN itself on these three single vectors, on the combination of the HC vector and ResNet and on the combination of HC and EfficientNet, obtaining the best results with the latter combination. In [136], the InceptionV3 network is used as a feature extractor (1000-dimensional vector obtained downstream of the penultimate layer of the network), and two different feed-forward neural networks (with two layers each and softmax activation function) for the classification of skin lesions into benign/malignant and melanocytic/non-melanocytic. The problem of limited and unbalanced data is addressed in [137], in which the authors propose an approach that improves the model's ability to handle these problems. The classifications of the six models are then merged with the metadata associated with the images in the dataset and sent to an SVM classifier. In this paper, the authors show that ensemble learning significantly improves classification accuracy even from low accuracies for individual models, and that TL and the use of metadata have only a minor effect on the result obtained. In [138], eight pretrained CNN models are used simultaneously to extract deep features from the images, and 10 different classifiers to perform the classification. The different couplings show that the DenseNet121 network with subsequent MPL achieves the highest performance in terms of accuracy. In [139], an ensemble method that combines several DL feature extractors of skin lesions with an SVM classifier with RBF kernel is proposed. Feature extraction is performed by using the pretrained AlexNet, VGG15, ResNet18, and ResNet101 models, replacing the last layer with an FC to perform a binary classification (MM, SK). The feature vectors are then classified with an SVM model whose scores were subsequently mapped into probabilities by using logistic regression.