## 4 Results
### Machine-Learning Methods  
A new algorithm for calculating the extended feature vector space is proposed in [74]. Specifically, features of color (from the hue-saturation value (HSV) space) and texture (via local binary pattern (LBP)) are extracted from the images and subsequently combined to extend the feature space. These features are then used by an ensemble bagged tree classifier for the detection of melanoma. In one paper [75], the authors, after smoothing the images with a Gaussian filter, use the active contour model to obtain the lesion edges from which they define a segmentation mask to extract lesion characteristics in terms of shape. From the mask, they replace the lesion pixels with those of the original image and then extract lesion characteristics in terms of color and texture. Finally, they use a K-nearest neighbor (KNN) model to perform the binary classification between the melanoma class and the seborrhoeic nevi-keratosis class. They obtain the best results with \(k=2\). In [76], lesion segmentation is performed in the gray space whereas in the RGB color space, texture characteristics are extracted with global (grey-level co-occurence matrix (GLCM) for entropy, contrast, correlation, angular second moment, inverse different moment, sum of squares) and local (LBP and oriented FAST and rotated BRIEF (ORB)) techniques. To extract color features from each image, histograms of the five color spaces (grayscale, RGB, YCrCb, L*a*b and HSV) are generated from which information on mean, standard deviation, skewness and kurtosis is obtained for a total of 52 color features for each image. In order to select only the most significant features, two variants of the Harris Hawk optimisation (HHO) algorithm are tested, employing S-shaped (BHHO-S) and V \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Architecture** & **Year** & **Developed by** & **Parameters** & **Layers** & **Input Size** \\ \hline GoogLeNet & 2014 & [PERSON] et al. & 4 M & 144 & 224 \(\times\) 224 \\ InceptionV3 & 2015 & [PERSON] et al. & 23.8 M & 316 & 299 \(\times\) 299 \\ ResNet18 & 2015 & [PERSON] et al. & 11.17 M & 72 & 224 \(\times\) 224 \\ ResNet50 & 2015 & [PERSON] et al. & 25.6 M & 177 & 224 \(\times\) 224 \\ ResNet101 & 2015 & [PERSON] et al. & 44.7 M & 347 & 224 \(\times\) 224 \\ SqueezeNet & 2016 & [PERSON] et al. & 1.2 M & 68 & 227 \(\times\) 227 \\ DenseNet201 & 2017 & [PERSON] et al. & 20.2 M & 709 & 224 \(\times\) 224 \\ Xception & 2017 & [PERSON] & 22.9 M & 171 & 299 \(\times\) 299 \\ Inception-ResNet & 2017 & [PERSON] et al. & 55.8 M & 824 & 299 \(\times\) 299 \\ EfficientNetB0 & 2019 & Mingving and Le & 5.3 M & 290 & 224 \(\times\) 224 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of common CNNs architectures. shaped (BHHO-V) transfer functions respectively, with BHHO-S leading to better results. As a final step, an SVM model is used to perform melanoma/non-melanoma classification on dermatological images. The authors of [77] use the lesion masks already provided with the images to extract 510 features (18 for shape, 72 for color and 420 for texture) which are then manipulated to create different subsets of features and sent to ensemble classification models that will use them to diagnose skin lesions. Each ensemble classification model is generated by using an optimum-path forest (OPF) classifier and integrated with a majority voting strategy. Three different approaches are proposed: SE-OPS, which manipulates features by using different subsets based on specific feature groups, SEFS-OPF, which manipulates features by using correlation-based features selection (CFS) to select the best features, and FEFS-OPS, which manipulates features by using different selection algorithms such as correlation coefficient information gain, principal component analysis (PCA) and CFS. The best model is found to be SE-OPF.