## 4 Results
### Deep-Learning Methods
This variant of triplet loss is used because, in contrast to the classical one that maintains a fixed distance from the origin independently for positive and negative classes, it adaptively updates the distance between clusters during the training procedure. The method seeks to maximise the distance between cluster centres instead of minimising the classification error by making the model less sensitive to imbalance between classes. Furthermore, to get away from the need for labels, an unsupervised approach is proposed by implementing COM-Triplet loss on pseudo-labels generated by Gaussian mixture model (GMM). The CNN has an architecture based on the backbones common in computer vision tasks (VGG16, ResNet50, DenseNet169 and EfficientNetB3) by replacing the dense layer with an embedding layer for deep clustering models. A dropout layer with a rate of 0.3 is also inserted between the backbone of the networks and this last layer. The best results are obtained by using the pretrained VGG16 network as the backbone and performing transfer learning. To automatically detect skin cancer on dermoscopic images, in [103] the authors use a metalearning method (also known as \"learning to learn\") that aims at understanding the learning process in order to use the acquired knowledge to improve the learning effectiveness of new tasks. The authors demonstrate that nonmedical image features can be used to classify skin lesions and that the distribution of data affects the performance of the model. They use a pretrained ResNet50 by removing the last dense layer and perform cross-validation three times. In [104], three pretrained networks (EfficientNet, SENet, and ResNet) are used in three different situations: training with preprocessed images, training with images multiplied by the segmentation mask obtained with the U-Net, and training with both of the previous solutions. The latter approach turns out to be the best in terms of accuracy. In [105], the MobileNet network pretrained on images from the ImageNet dataset and optimised on dermatological images is used. Several pre-trained neural networks (PNASNet-5-Large, InceptionResNetV2, SENet154 and InceptionV4) are being tested in [106], freezing all levels except the last FC where the softmax function is used to produce the probability of each class. The best result in terms of accuracy is achieved by the model based on the PNASNet-5-Large network. In [107], transfer learning (TL) is performed on the VGG16 and GoogLeNet networks, which are evaluated both individually and combined. The best result is obtained with the combination of the two models. A multitask deep learning model is proposed in [108]. This model consists of three parallel layers: a segmentation branch that returns the lesion mask, a binary classification branch for melanoma detection, and a binary classification branch for seborrhoeic keratosis detection. The input of the network are the images to which several labels describing different lesion characteristics are associated, whereas the output provides the binary mask of the lesion, the probability of belonging to the melanoma class, and the probability of belonging to the seborrhoeic keratosis class. The model is implemented based on the GoogLeNet architecture, which is common to all three branches; the U-Net is used for segmentation and two FC layers are added for the classification branches. In [109], a new DL architecture called NABLA-N Network for lesion segmentation, and the inception recurrent residual convolutional neural network (IRRCNN) model for skin cancer lesion classification are proposed. The classification network consists of three recurrent residual units followed by subsampling layers. At the end of the model, a GAP layer is used, which helps to significantly reduce the number of network parameters compared to a FC layer, followed by a softmax layer. The model is evaluated with and without DA, showing that performance increases significantly in the latter case.