## 4 Results
### Deep-Learning Methods
In [95], a new DL model is proposed based on the VGG16 architecture by eliminating some redundant convolutional layers, introducing a batch normalisation (BN) layer after each pooling layer and replacing the FC layer with a global average pooling (GAP) layer. Eliminating some convolutional layers decreases the trainable parameters and introducingBN and GAP layers improves performance without increasing the number of parameters. By decreasing the network parameters compared to VGG16, the entire architecture is optimised and calculation times are accelerated. With the idea of improving the existing performance measures and minimising the convergence time of the learning model in the skin cancer detection task, in [96] the authors use AlexNet as a pretrained architecture and replace its larger filters with smaller ones. This reduces the parametric complexity of the model but increases its depth, causing the vanishing gradient phenomenon during the training phase. To overcome this problem, residual or skip connections are introduced through several pairs of consecutive blocks (taking a cue from ResNet). Finally, learning rate annealing is applied by using the cyclic learning rate during training. In [97], adversarial training is used to achieve good accuracy in skin tumour classification, despite having a small amount of data available. By applying the fast gradient sign method (FGSM), new adversarial example images are created to maximise the loss for the input image, which are subsequently used in both train and test phases. With these new images, some pretrained networks (VGG16, VGG19, DenseNet101, and ResNet101) are retrained, and ResNet101 obtains the best results even though it consumes more computational power and takes longer than the others. In [98], the authors use the pretrained ResNet52 network in five different situations to classify skin lesions. The tests performed are: training without data augmentation (DA), training with DA only on malignant images, training with DA on malignant and downsampling (DS) of benign images with two different proportions, training with DA only on malignant images by including other images from different datasets in the dataset. The best solution appears to be the one in which the data is augmented only on lesions belonging to the malignant class while maintaining a malignant/benign ratio of 0.44. In [99], the VGG16 network is used in three different ways: training from scratch, transfer learning, and fine tuning. The training-from-scratch approach turns out to be the least accurate of the three proposed. The TL method greatly outperforms the former but shows very different performance between the training and test phases, testifying to the presence of overfitting. Applying fine-tuning results in the best model with superior performance to the former approach and no evidence of overfitting on the train data. A new approach that not only classifies skin lesions with DL models but also discriminates is proposed in [100]. An architecture is created to take a pair of images (malignant/benign) as input and use a light network pretrained on the ImageNet dataset to extract two feature vectors, one from each image, used individually to train two networks for melanoma recognition, and jointly to introduce a nonparametric discriminant layer through which a network is constructed to check whether or not the images corresponding to the two jobs belong to the same category. In [101], after performing image preprocessing, segmentation and DA, they use the ResNet50 and InceptionV3 networks pretrained on ImageNet to perform binary classification of skin lesions. The final result is obtained by averaging the predictions generated by each classifier. A deep clustering approach based on the incorporation in latent space of dermoscopic images of skin lesions is proposed in [102]. To learn discriminative embeddings, clustering is achieved by using a novel centre-oriented margin-free triplet loss (COM-Tripletenforced on image embedding from a CNN backbone).