## 4 Results
### Deep-Learning Methods
In [110], multiple TL models based on XceptionNet, DenseNet201, ResNet50, and MobileNetV2 are tested. After training with the preprocessed and augmented images, the best model in terms of accuracy, precision, recall, and F1 score is the one based on ResNet50. A combination of a multilabel deep feature extractor (ResNet50 backbone) with a clinically constrained classification chain to formulate the seven-point checklist algorithm based on the major and minor criteria and their respective weightings used by dermatologists is proposed in [111]. Each input, consisting of a clinical and a dermoscopic image, is associated with a label for the diagnosis (melanoma/non-melanoma) and seven labels for the evaluation criteria scores. Image features are extracted from the network, reduced in dimensionality by PCA, concatenated, and sent to the grading chain to obtain predictions on all seven-point checklists. The final score is the sum of all predictions weighted by the respective clinical weights (weight = 2 for major criteria and weight = 1 for minor criteria). A score greater than or equal to 3 produces a diagnosis of melanoma. By keeping the criteria of the seven-point analysis, the proposed system could be more accepted by dermatologists as a human-interpretable CAD tool for automated melanoma detection. In [112], several features are extracted from the skin lesions and a subsequent feed-forward neural network is used to perform classification by using the Levenberg Marquardt generalisation method (LM) to minimise mean square error. The extracted features are mean, standard deviation and skewness; entropy, mean and energy using the discrete 2D wavelet transform; contrast, similarity, energy and homogeneity using the GLCM. A mixed skin lesion picture generated method based on Mask R-CNN (MSLP-MR) is implemented in [113] to augment the class of melanomas and reduce data imbalance. The augmented dataset is used to train models such as InceptionV4, ResNet, and DenseNet121, of which the latter is the best. Based on this observation, the DenseNet network is deepened by creating the DenseNet architecture145. In [114], the optimal deep neural network driven computer-aided diagnosis for skin cancer detection and classification (ODNNsingle bondCADSCC) model is designed, which applies preprocessing based on Wiener filtering (WF), performs lesion segmentation with U-Net and extracts features with SqueezeNet. Finally, the improved whale optimization algorithm (IWOA) selects the parameters of the feed-forward DNN (FFNN) with three hidden layers that will be used for the effective detection and classification of skin cancer. For the classification of melanoma, the TL on the SqueezeNet is used in [115], the optimal parameters of which are identified by using the bald eagle search (BES) method. In addition, a random oversampling method (ROS) followed by data augmentation is used to eliminate data imbalance. This approach, in addition to yielding excellent results in terms of accuracy, sensitivity, specificity, F1 score, and AUC, requires less training time than other pretrained networks including VGG19, GoogleNet, and ResNet50. In [116], the authors propose a study on the effect of image size for skin lesion classification based on pretrained CNNs and transfer learning. After examining the classification performance of three well-established CNNs, namely EfficientNetB0, EfficientNetB1, and SeReNeXt-50, it is shown that image cropping is a better strategy than scaling and provides superior classification performance at all image scales from \(224\times 224\) to \(450\times 450\). Furthermore, for the classification of skin lesions the authors of the paper propose and evaluate a unique multiscale multi-CNN (MSM-CNN) fusion approach, which consists of assembling the results of the three different fine-tuned networks, trained with cropped images at six different scales.