## 4 Experimental Results and Discussion
### Randomization of Model Weights  
In this experiment, using the same architectures as for the re-trained models described in Section 3.3, different network models have been generated by initializing their weights randomly using the initialization proposed by [47], while the bias vectors have been initialized to zero.  
Given that our test set contains 148 images, we constructed two vectors of Shapley values, each with 1036 elements (7 sectors \(\times\) 148 images), to compute the correlation. One vector corresponds to the randomized model and the other to the re-trained model. By doing so, we have a correlation value for each partition per architecture. These 5 values are the ones we have used to calculate each box-and-whiskers diagram in Figure 5, where we present the correlations for each architecture between the randomized and the re-trained models.  
It can be observed that both the medians of the correlations and their extreme values for each architecture present absolute values, in general, lower than 0.1, except for the maximum value of Xception which reached 0.25. These values are therefore within the expected range.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Network** & **Fold** & **Sensitivity** & **Specificity** & **Accuracy** & **B. Accuracy** & **F1 Score** \\ \hline VGG19 & 1 & 0.8429 & 0.7742 & 0.8218 & 0.8085 & 0.8676 \\ VGG19 & 2 & 0.8857 & 0.7419 & 0.8416 & 0.8138 & 0.8857 \\
**VGG19** & **3** & **0.8429** & **0.8065** & **0.8317** & **0.8247** & **0.8741** \\ VGG19 & 4 & 0.8857 & 0.7419 & 0.8416 & 0.8138 & 0.8857 \\ VGG19 & 5 & 0.9571 & 0.6452 & 0.8614 & 0.8012 & 0.9054 \\ VGG19 & M \(\pm\) SD & 0.8829 \(\pm\) 0.0467 & 0.7419 \(\pm\) 0.0603 & 0.8396 \(\pm\) 0.0147 & 0.8124 \(\pm\) 0.0086 & 0.8837 \(\pm\) 0.0144 \\ \hline
**ResNet50** & **1** & **0.9286** & **0.7742** & **0.8812** & **0.8514** & **0.9155** \\ ResNet50 & 2 & 0.8571 & 0.7419 & 0.8218 & 0.7995 & 0.8696 \\ ResNet50 & 3 & 0.8143 & 0.7419 & 0.7921 & 0.7781 & 0.8444 \\ ResNet50 & 4 & 0.9143 & 0.7419 & 0.8614 & 0.8281 & 0.9014 \\ ResNet50 & 5 & 0.9429 & 0.6774 & 0.8614 & 0.8101 & 0.9041 \\ ResNet50 & M \(\pm\) SD & 0.8914 \(\pm\) 0.0540 & 0.7355 \(\pm\) 0.0353 & 0.8436 \(\pm\) 0.0360 & 0.8135 \(\pm\) 0.0279 & 0.8870 \(\pm\) 0.0293 \\ \hline InceptionV3 & 1 & 0.8429 & 0.8065 & 0.8317 & 0.8247 & 0.8741 \\ InceptionV3 & 2 & 0.8571 & 0.7419 & 0.8218 & 0.7995 & 0.8696 \\ InceptionV3 & 3 & 0.9000 & 0.7097 & 0.8416 & 0.8048 & 0.8873 \\ InceptionV3 & 4 & 0.8571 & 0.7742 & 0.8317 & 0.8157 & 0.8759 \\
**InceptionV3** & **5** & **0.8857** & **0.7742** & **0.8515** & **0.8300** & **0.8921** \\ InceptionV3 & M \(\pm\) SD & 0.8686 \(\pm\) 0.0235 & 0.7613 \(\pm\) 0.0368 & 0.8356 \(\pm\) 0.0113 & 0.8149 \(\pm\) 0.0128 & 0.8798 \(\pm\) 0.0095 \\ \hline Xception & 1 & 0.8429 & 0.7097 & 0.8020 & 0.7763 & 0.8551 \\ Xception & 2 & 0.8714 & 0.6774 & 0.8119 & 0.7744 & 0.8652 \\
**Xception** & **3** & **0.8286** & **0.7419** & **0.8020** & **0.7853** & **0.8529** \\ Xception & 4 & 0.9143 & 0.6452 & 0.8317 & 0.7797 & 0.8828 \\ Xception & 5 & 0.8857 & 0.6774 & 0.8218 & 0.7816 & 0.8732 \\ Xception & M \(\pm\) SD & 0.8686 \(\pm\) 0.0341 & 0.6903 \(\pm\) 0.0368 & 0.8139 \(\pm\) 0.0129 & 0.7794 \(\pm\) 0.0043 & 0.8659 \(\pm\) 0.0125 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained by evaluating the models with the DRISHTI-GS1 dataset, according to different metrics. Results corresponding to the maximum balanced accuracy (B. Accuracy) are highlighted in bold. The last row of each architecture shows the mean (M) \(\pm\) the standard deviation (SD) for every metric.