## 4 Results
### Deep-Learning Methods  
In [85], a parameter transfer of a pretrained network to a CNN is performed to reduce the training time. The performance of the network without and with fine tuning (FT) is compared, obtaining better results in the second option. For the melanoma detection task, an ensemble learning approach is proposed in [86] to combine the predictive power of three different deep convolutional neural network (DCNN) models known from medical imaging classifications pretrained on the ImageNet dataset: EfficientNet88, SEResNeXt10, and DenseNet264. Two innovative approaches are used: the multisample dropout approach, whereby, downstream of the pre-trained network architectures, the dropout, fully connected (FC), and softmax layers are duplicated and the loss value (obtained by using a variant of the binary cross-entropy called focal loss to perform dense object detection) is calculated as the average of the loss values of all dropout samples, and, secondly, the multi-penalty approach, whereby each duplicated layer is penalised at a different rate. Moreover, in [87], an ensemble learning approach is used. An ensemble of deep model (SLDEP) is created by using four different CNNs (GoogLeNet, VGGNet, ResNet, and ResNeXt) to perform multiclass classification based on majority voting. In [88], the authors perform a multiclass classification by using TL on InceptionV3, ResNet50, and Denset201, removing the output layer from these architectures and adding pooling and FC layers. In [89], the authors, after preprocessing the images to remove hair and improve image quality by using the HR-IQE (hair-removal image-quality enhancement) algorithm, proceed with lesion segmentation by using swarm intelligence (SW) algorithms to identify the region of interest (ROI), extract features within the ROI by using sped-up robust features (SURF) and select only a few of these based on the grasshopper optimisation algorithm (GOA). Finally, a custom CNN, consisting of two convolutional layers followed by two max-pooling layers, and a flatten layer, is used to classify images into melanoma and nonmelanoma classes. In [90], an AWO-based SqueezeNet is proposed in which the pre-trained SqueezeNet is trained by a proposed AWO algorithm which is a fusion of the aquila optimisation (AO) algorithm and the whale optimisation algorithm (WOA). In [91], a custom CNN with five convolutional layers, five max pooling layers, two dense layers and one dropout layer is used. The authors focus heavily on image preprocessing work to enable the network to achieve better performance. In [92], various types of CNNs (ResNet, DenseNet, InceptionV3, VGG16) pretrained on ImageNet are implemented to evaluate their performance in the skin cancer diagnosis task. After selecting some features of the InceptionV3 and DenseNet architectures, a new architecture called DenseNet-II is built in which there are two parallel networks of convolutional layers. By using focal loss, they create an imbalance of weights to penalise the majority class and reduce the damaging effects of class imbalance. In [93], a shallow DL model called SCNN\({}_{12}\) is created, consisting of 12 weighted layers: 4 convolutional, 4 max pooling, 1 flatten, 2 dense, and 1 softmax layer. The ablation study method is used to determine the parameters and hyperparameters of the model on the basis of optimal performance in terms of accuracy. In addition to classical preprocessing operations, the authors perform downsampling by reducing the spatial resolution of the images while keeping the size unchanged. In this way, the images retain their 224 \(\times\) 224 size but are reduced from 45 kb to 6 kb spatial resolution. An early skin cancer detection approach using a pretrained DL model is proposed in [94]. In this work, a Flask website is also developed to allow users to upload dermatological images and make a prediction on the class they belong to.