## 4 Results
### Deep-Learning Methods
After each of the three models (EfficientNetB0, EfficientNetB1 and SeReNeXt-50) has performed a prediction on the cropped images in six different formats, the average of the six classifications for each network is obtained and then the three final results are averaged again to obtain the final classification. In [117], the authors propose a multiclass multilevel classification algorithm (MCML) for multiclass (healthy, benign, malignant, and eczema) classification of skin lesions and evaluate the use of traditional machine learning and an advanced deep learning aproach. In the first approach, after the steps of preprocessing, segmentation and feature extraction, an ANN with three hidden layers is used to perform classification. In the second approach, the TL is used, and a pretrained AlexNet model is modified, fine-tuned and retrained on the dermatology dataset. The best results are obtained with the DL approach. In [118], a new deep-learning methodology is proposed to implement effective skin disease classification. After preprocessing and image segmentation, deep features are extracted by using Resnet50, VGG16, and Deeplabv3 and then concatenated. These concatenated features are transformed by using hybrid squirrel butterfly search optimization (HSBSO) and then passed to modified long short-term memory (MLSTM), where architecture optimization is performed by HSBSO itself to produce the final classified output. This paper [119] proposes a self-supervised topology clustering network (STCN) by a transformation-invariant network with self-supervised maximum modularity clustering algorithm following topology analysis principle. A pre-trained ResNet50 is used as a feature-extraction module, and the image decoder in cycle GAN is used as a self-expression module. Finally, the feature vectors of the images are used to train a deep topology clustering algorithm that performs clustering, and a softmax layer is added downstream of the feature vector to make the entire network capable of performing classification. The authors in [120] propose a deep convolutional neural network (DCNN) model to perform accurate classification of skin lesions into malignant and benign. The CNN is pretrained on a large image dataset (ImageNet), and then fine tuned to a new dermatological dataset. In testing, the proposed model achieves good performance in terms of accuracy, precision, recall, and F1 score. This model is found to be more accurate when the pathology is in an early stage. In [121], the authors present a framework for skin cancer classification that combines image preprocessing with a hybrid-CNN. The proposed CNN consists of three feature extraction blocks. The feature maps output from these blocks are sent to an FC layer either individually or concatenated with each other. Finally, the results are merged to provide the overall output. In [122], a novel deep learning framework for segmentation and classification of skin lesions is proposed. In the classification phase, a 24-layer convolutional neural network architecture is designed, the best features of which are provided to softmax classifiers for final classification. In [123], an average ensemble learning-based model is proposed to use five pretrained deep neural network models (ResNeXt, SeResNeXt, ResNet, Xception, and DenseNet) as the basis of the ensemble to classify seven types of skin lesions. The grid search method is used to find the best combination of the basic models and perform a weighted average combination, but it is shown that the models all behave more or less the same, except for DenseNet, and therefore the unweighted average combination can be used. A novel deep convolutional neural network for the melanoma and seborrheic keratosis detection task is presented in [124]. The novelty of this approach is to use a pretrained ResNet18 network for classification of the original images, and four other pretrained AlexNet networks for classification of four new images obtained by applying Gabor wavelet filters with coefficients 0, 45, 67.5, and 112.5.