# Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances
## 4 Experimental Evaluation
### Classification of Skin Cancer Instances  
In this section, we present the evaluation of the seven classes together as a seven-class problem. The evaluation is based on 10-fold cross-validation of the seven classes. Table 1 and Figures 3 and 4 shows the evaluation results in different representations. Table 1 shows the accuracy of six different classifiers (BN, NB, SVM, KNN, J48, and RF) on six types of features (BP, CL, EH, FoH, JPEG-C, and PHoG) extracted from images from the Skin Disease Classification dataset. The accuracy is the percentage of correctly classified images out of the total number of images. The higher the accuracy, the better the performance of the classifier. From the table, we can see that RF has the highest accuracy for all feature types, ranging from 71.5% to 81.85%. This means that RF can distinguish between infected images better than the other classifiers. The lowest accuracy for RF is achieved with CL features, which are color histograms that capture the color distribution of the images. The highest accuracy for RF is achieved with BP features, which are binary patterns that capture the texture information of the images. KNN, which has an accuracy range of 67.4% to 74.45%, is the second best classifier. KNN is a straightforward classifier that gives a picture a label based on the labels of its closest neighbors in the feature space. The lowest accuracy for KNN is achieved with CL features as well, while the highest accuracy is achieved with JPEG-C features, which are compressed versions of the original images. The third best classifier is SVM, which has an accuracy range of 67.2% to 71.05%. SVM is a classifier that finds a hyperplane that separates the images into two classes with the maximum margin. The lowest accuracy for SVM is achieved with CL features again, while the highest accuracy is achieved with BP features. The fourth best classifier is J48, which has an accuracy range of 63.7% to 71.05%. J48 is a classifier that builds a decision tree based on splitting criteria that maximize information gain. The lowest accuracy for J48 is achieved with CL features once more, while the highest accuracy is achieved with BP and EH features. EH features are edge histograms that capture the edge orientation and frequency of the images. The fifth best classifier is NB, which has an accuracy range of 34.4% to 58%. NB is a classifier that assumes conditional independence between the features and applies [PERSON] theorem to calculate the posterior probabilities of the classes. The lowest accuracy for NB is achieved with JPEG-C features, while the highest accuracy is achieved with EH features. The worst classifier is BN, which has an accuracy range of 36.25 to 60.9%. BN is a classifier that models the joint probability distribution of the features and classes using a directed acyclic graph. The lowest accuracy for BN is achieved with JPEG-C features as well, while the highest accuracy is achieved with FoH features. FoH features are Fourier histograms that capture the frequency spectrum of the images. From the Table 1, the RF outperforms all other classifiers on all feature types for skin cancer classification. We can also see that CL features are not very effective for this task, as they have low accuracy for all classifiers. On the other hand, BP features are very effective for this task, as they have high accuracy for most classifiers. Figure 3 shows the graph showing the average accuracy. The results show the average performance of six different classifiers (BN, NB, SVM, KNN, J48, and RF) on six types of features (BP, CL, EH, FoH, JPEG-C, and PHoG) extracted from images of skin cancer for the seven classes. The performance is measured by accuracy, which is the percentage of correctly classified instances out of the total number of instances. The higher the accuracy, the better the performance of the feature type.