## 4 Experimental Results  
Table 4 shows all the information provided by the runs performed. In addition to the Accuracy and AUC metrics considered in the analysis of ANOVA, the complementary measures for Precision and Recall are also provided. To perform the statistical study, a selection is made from a set of alternatives representative of each of the factors considered for performing the deep learning training procedure. The factors considered in the analysis are listed in Table 5. By analyzing the different levels of each of these factors, it is possible to determine their influence on the performance of the analyzed system (accuracy) when different alternatives for preprocessing the images and also different deep learning models are presented. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Metrics** & **Models** & **Raw** & **Blur** & **CLAHE** & **Contour** & **Detail** & **Edge** & **Edge Enhance** & **FHE** & **HE** & **Sharpen** \\ \hline \multirow{4}{*}{Accuracy} & VCG16 & 86.03 \(\pm\) 0.17 & 85.69 \(\pm\) 0.23 & **86.22 \(\pm\) 0.35** & **85.62 \(\pm\) 0.30** & 86.85 \(\pm\) 0.22 & **86.48 \(\pm\) 0.35** & 86.22 \(\pm\) 0.45 & **86.48 \(\pm\) 0.37** & **85.51 \(\pm\) 0.16** & 86.85 \(\pm\) 0.27 \\  & VGG19 & 86.84 \(\pm\) 0.15 & 85.73 \(\pm\) 0.12 & 86.24 \(\pm\) 0.25 & 85.80 \(\pm\) 0.33 & 86.55 \(\pm\) 0.28 & 86.38 \(\pm\) 0.30 & **86.25 \(\pm\) 0.31** & 86.39 \(\pm\) 0.17 & 85.63 \(\pm\) 0.34 & 86.71 \(\pm\) 0.23 \\  & ResNet50 & **86.96 \(\pm\) 0.16** & **86.00 \(\pm\) 0.05** & 85.89 \(\pm\) 0.17 & 85.39 \(\pm\) 0.33 & **86.58 \(\pm\) 0.10** & 86.31 \(\pm\) 0.22 & 85.74 \(\pm\) 0.04 & 86.39 \(\pm\) 0.07 & 85.25 \(\pm\) 0.09 & **86.95 \(\pm\) 0.14** \\  & ResNet50 & 86.44 \(\pm\) 0.08 & 85.53 \(\pm\) 0.25 & 85.43 \(\pm\) 0.27 & 86.16 \(\pm\) 0.15 & 86.09 \(\pm\) 0.40 & 85.55 \(\pm\) 0.05 & **85.84 \(\pm\) 0.17** & 84.93 \(\pm\) 0.09 & 86.88 \(\pm\) 0.08 \\  & DenseNet121 & 85.62 \(\pm\) 0.12 & 84.69 \(\pm\) 0.19 & 84.66 \(\pm\) 0.18 & 83.88 \(\pm\) 0.20 & 85.35 \(\pm\) 0.36 & 84.89 \(\pm\) 0.13 & 84.43 \(\pm\) 0.01 & 85.11 \(\pm\) 0.12 & 84.23 \(\pm\) 0.23 & 85.83 \(\pm\) 0.20 \\ \hline \multirow{4}{*}{Precision} & VGG16 & 87.99 \(\pm\) 0.42 & 86.34 \(\pm\) 0.52 & **86.99 \(\pm\) 1.23** & 85.20 \(\pm\) 0.50 & 86.23 \(\pm\) 1.23 & 85.66 \(\pm\) 1.26 & 85.80 \(\pm\) 1.45 & **86.34 \(\pm\) 0.46** & **86.35 \(\pm\) 1.21** & 86.39 \(\pm\) 1.09 \\  & VGG19 &Table 6 gives the two-way variance analysis for the whole set of processing examples of the deep learning system analyzed in this contribution. The ANOVA table containing the sum of squares, degrees of freedom, mean square, test statistics, etc., represents the initial analysis in a compact form. This kind of tabular representation is customarily used to set out the results of the ANOVA calculations. The table ANOVA decomposes the variability of the AUC into contributions due to different factors. Because the sums of squares are of type III (the default), the contribution of each factor is measured after the effects of all other factors have been removed. The P-values test the statistical significance of each factor. Since a P-value is less than 0.05, this factor has a statistically significant effect on the AUC at a 95% confidence level. Of all the information presented in the table ANOVA, the researcher's main interest is likely to be focused on the value in the \"F-ratio\" and \"_p_-value\" columns If the numbers in this column are smaller than the critical value set by the experimenter, the effect is considered significant. This _p_-value is often set at 0.05. Any value lower than this leads to significant effects, while any value higher than this leads to non-significant effects [35]. When effects are found to be significant using the procedure described above, it means that there is more difference between the means than would be expected from chance alone. With respect to the experiment above, this would mean that the preprocessing filter of the WSI images has no significant effect on the accuracy of the system.