## 6 Results
Figure 4: Results of preprocessing: (**a**) Original image. (**b**) Gaussian filter. (**c**) Histogram Equalization. (**d**) Gaussian filter + Histogram Equalization. (**e**) Color Normalizaion. (**f**) Gaussian filter + Color Normalizaion. (**g**) Histogram Equalization + Color Normalizaion. made to balance the two classes was the best one. In this regard, the proposed balancing situation--the results of which have already been shown in Table 4--is compared with the following two: (1) the imbalance between the classes is not balanced, and therefore, the training set includes all nevi and melanoma images (scenario named M/Ntot). In this case, the number of nevi is more than three times the number of melanomas; (2) to make the number of melanomas approach the total number of nevi, data augmentation is applied on the melanoma images until the number of melanomas is tripled (scenario named DA Mx3). The operations performed to increase the number of images in the training dataset are: rotation (range = 20), width shift (range = 0.1), height shift (range = 0.1), shear (range = 0.3), zoom (range = 0.3), horizontal flip, and fill mode = 'nearest'. The results obtained by training the network with these new datasets and the comparison without using DA are shown in Table 7.  
Paradoxically, by not balancing the classes and thus having one-third as many melanomas as nevi, the specificity is much higher than the sensitivity; on the other hand, as the number of melanomas increases, the ability of the network to recognize nevi also increases, but the network has zero sensitivity, indicating that no melanomas were classified correctly. The results obtained, although it may not seem like it, are \"lucky\" results; in fact, they could have been even worse considering the fact that during training, the performance of the models on the validation dataset was anything but consistent in both scenarios previously described. Figure 5 plots the accuracy curves obtained during training in the M/Ntot Figure 5a and DA Mx3 Figure 5b situations. Having verified that the best classification results for melanoma detection are obtained by adding the texture information captured using the LBP operator to the CNN network and dividing the two classes equally without applying DA techniques, we refer to this situation below by talking about the \"proposed work\". Then, the accuracy and loss plots of the training and validation sets related to the proposed work are shown in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **M** & **N** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline M/Ntot & 5635 & 19,580 & 45.00 & 73.68 & 39.60 & 56.64 \\ DA Mx3 & 16,905 & 19,580 & 84.17 & 0.00 & 100.00 & 50.00 \\ CNN LBP & 5635 & 5636 & 82.50 & 76.61 & 83.61 & 80.11 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of results obtained using different distributions of M and N in the training dataset. Performance results are in percentages. Figure 5: Model training accuracy graphs. (**a**) M/Ntot accuracy graph. (**b**) DA Mx3 accuracy graph. The model shows an excellent learning rate, as the training accuracy increases with the number of epochs (Figure 5(a)), and the validation accuracy, although it tends to decrease at first, stabilizes not far from the training curve. Both training loss and validation loss decrease to the point of stability (Figure 5(b)). Training reaches convergence after about 50 epochs. The small gap between the training and validation curves shows that the model is not affected by overfitting. The results shown by the validation curves are very different from those obtained on the test set (see Table 7).