## 6 Results
For all ML methods, \"best result\" means the one that shows the best trade-off between high sensitivity and specificity. The results obtained on the test dataset in the different situations are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**Before**} & \multicolumn{3}{c}{**After**} \\ \hline  & **N** & **M** & **Tot** & **N** & **M** & **Tot** \\ \hline ISIC 2018 & 6705 & 1113 & 7818 & 2818 & 1113 & 3931 \\ ISIC 2019 & 12,875 & 4522 & 17,397 & 2818 & 4522 & 7340 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Number of N and M samples in training datasets. The ML methods show similar sensitivity values to those obtained with the various CNN networks, but they have lower specificity, accuracy, and AUC. Looking at the results obtained with CNNs, it is evident from the results that none of the situations is clearly superior to the others, but the best is the one in which LBP texture features are injected into the proposed CNN network. For these latter four situations, ROC curve plots are shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline SVM GLCM & 64.54 & 76.02 & 62.38 & 69.20 \\ KNN GLCM & 63.70 & 64.33 & 63.59 & 63.96 \\ LDA GLCM & 66.39 & 63.74 & 66.89 & 65.31 \\ \hline SVM LBP & 74.26 & 69.59 & 75.14 & 72.36 \\ KNN LBP & 70.83 & 70.76 & 70.85 & 70.80 \\ LDA LBP & 75.83 & 62.57 & 78.33 & 70.45 \\ \hline SVM G\&L & 78.52 & 74.27 & 79.32 & 76.79 \\ KNN G\&L & 72.50 & 74.85 & 72.06 & 73.46 \\ LDA G\&L & 79.44 & 73.68 & 80.53 & 77.11 \\ \hline CNN w. f. & 81.11 & 75.44 & 82.18 & 78.81 \\ CNN G\&L & 81.57 & 68.42 & 84.05 & 76.23 \\ CNN GLCM & 80.83 & 75.44 & 81.85 & 78.64 \\ CNN LBP & 82.50 & 76.61 & 83.61 & 80.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different situations for the classification of melanoma vs. nevus: ML methods for the classification of GLCM features; ML methods for the classification of LBP features; ML methods for the classification of both GLCM and LBP features (G&L); CNN without the injection of handcrafted features (CNN w.f. ); CNN with the injection of both GLCM and LBP features (CNN G&L); CNN with the injection of only GLCM features (CNN GLCM); and CNN with the injection of only LBP features (CNN LBP). All values are in percentages. Figure 3: ROC curves. One might also ask whether the LBP features extracted from the images are all relevant or whether a selection of the most significant ones should be performed. In this regard, two alternative scenarios were simulated in which Principal Component Analysis (PCA) was applied to reduce the dimensionality of the features to 1/3 and 1/2 of their original sizes. The results obtained by training the proposed CNN with the addition of these reduced features are shown in Table 5 along with those obtained by considering all the original features. In the PCA(1/3) case, sensitivity increases compared with the situation where feature dimensionality is not reduced, but specificity decreases greatly. In contrast, in the PCA(1/2) case, the results are lower than the CNN LBP situation for all metrics. From the results obtained, it can be inferred that it is not necessary to perform dimensionality reduction of LBP texture features since they are all important for classification of skin lesions. In addition, the present work aims to investigate whether a preliminary image preprocessing step is necessary for the addressed anomaly detection task. In this regard, a comparison is made between the performance obtained by training the custom CNN LBP with the original images and with images preprocessed using the main techniques used in the field of dermatology, as is discussed briefly below. * Gaussian filter (GF) is a linear smoothing filter that operates as a kind of low-pass filter [70]. It is a widely used preprocessing method in image processing and computer vision to attenuate noise.