## 6 Results
In the present work, the standard deviation for the Gaussian kernel (\(\sigma\)) is set to 1, 3, and 5; the smoothing operation is performed identically in all directions, resulting in a filtering action independent of the orientation of the structures in the image. * Histogram Equalization (HE) is the most widely used global method for calibrating contrast in images and is based on the idea of reassigning pixel intensity values to make the intensity distribution maximally uniform [71]. * Color Normalization (CN) is a technique widely used in computer vision to compensate for illumination variations in images. The method chosen for the present work is based on the _technique of color constancy_[72] that ensures that the perceived color of the image remains the same under different illumination conditions in order to facilitate the classification algorithm. Various combinations of the techniques just described were also tested: GF + HE, GF + CN, and HE + CN. In such combinations, the Gaussian filter was set with \(\sigma=1\), which has been shown to yield the best classification results. The results of applying the various preprocessing methods to a skin lesion image are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline PCA(1/3) & 82.41 & 60.82 & 86.47 & 73.64 \\ PCA(1/2) & 82.31 & 71.35 & 84.38 & 77.86 \\ CNN LBP & 82.50 & 76.61 & 83.61 & 80.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of classification results obtained by training the CNN network with all the LBP features and with a portion of them (PCA(1/3) and PCA(1/2)). All values are in percentages. A comparison of the classification results obtained by training the neural network with original and preprocessed images is shown in Table 6.  
It can be seen from the results that: (1) the Gaussian filter with \(\sigma=3\) and \(\sigma=5\) leads to high specificity values but very low sensitivity; (2) color normalization (CNN CN), Gaussian filtering combined with histogram equalization (CNN GF + HE), and the combination of histogram equalization and color normalization (CNN HE + CN) allows for the obtainment of higher specificity values--and therefore higher accuracy--than that obtained without preprocessing; however, in all these situations, the sensitivity, which reflects the accuracy in correctly classifying melanomas and is therefore a parameter of utmost importance for the specific application (anomaly detection in the medical field), is much lower than that obtained with CNN w. p.; (3) application of the Gaussian filter with \(\sigma=1\) leads to classification results similar to those obtained by training the network with the original images, but lower; (4) the best trade-off between sensitivity, specificity and accuracy seems to be achieved by the CNN network trained using the original images. Once the best solution for melanoma detection was identified--which was obtained by adding the texture information captured by the LBP operator from the unprocessed images to the CNN network--we wanted to check whether the selection of the training dataset \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline CNN GF (\(\sigma=1\)) & 80.09 & 71.35 & 81.74 & 76.54 \\ CNN GF (\(\sigma=3\)) & 84.07 & 0 & 99.89 & 49.95 \\ CNN GF (\(\sigma=5\)) & 83.43 & 40.35 & 91.53 & 65.94 \\ CNN HE & 77.59 & 77.78 & 77.56 & 77.68 \\ CNN CN CN & 83.98 & 67.25 & 87.13 & 77.19 \\ CNN GF + HE & 83.89 & 63.74 & 87.68 & 75.71 \\ CNN GF + CN & 75.93 & 81.87 & 74.81 & 78.34 \\ CNN HE + CN & 83.61 & 63.13 & 87.46 & 75.31 \\ CNN LBP & 82.50 & 76.61 & 83.61 & 80.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of classification results obtained by training the CNN network with preprocessed and original images (CNN LBP is the CNN trained with original images, i.e., images without preprocessing). All values are in percentages.