## 4 Results and Discussion
### Sensitivity of the Proposed Approach to the Parameter Settings
The results have been obtained by using \(N_{T}=500\) reference images from the N class and a number \(N_{bin}=50\) of histogram bins. We also evaluate the robustness of the proposed approach with respect to the number \(N_{T}\) of target images. Towards this end, we evaluate the target histogram by averaging \(N_{T}\) histogram representations of the corresponding scans. Table 7 summarizes the obtained results using the cosine distance in (3). In this test, we again used \(N_{bin}=50\) histogram bins, while the threshold in (2) has been set to \(\eta=2\) for the CT dataset and to \(\eta=1.4\) for CXR one, respectively. Table 7 shows that, especially for the CT dataset, the performance of the proposed approach is quite insensible to the choice of the number \(N_{T}\) of target images in a large interval, since performance remains unchanged, even if the standard deviation tends to decrease by increasing the number of used images, because this depends on their average. However, we can see a gradual degradation in performance by reducing the number \(N_{T}\) of target images. On the basis of these results, we used 500 target images to maintain the standard deviation at an appropriate lower value. The last test on the sensitivity of the proposed approach is concerned with the choice of the \(\eta\) parameter in Equation (2). To this end, we vary the \(\eta\) parameter inside the interval \([0,\,5]\), with a step-size equal to 0.1, for a total of 51 different values, and we graphically report the corresponding obtained accuracy (measured in percentage). Figure 4 shows the results for both the considered datasets. These results have been evaluated by using the cosine distance, a number \(N_{T}=500\) of target images, and a number \(N_{bin}=50\) of histogram bins. Figure 4 clearly shows that there is a suitable range of \(\eta\) values that produces the accuracy of 100%, while the performance rapidly decreases with vanishing \(\eta\) (that means the threshold in Equation (2) is set on the basis of the mean distance \(d_{m}\) only) or with increasing \(\eta\) values. Figure 4 also shows that this range is independent of the used dataset, since its corresponding value for the CT is larger than that of the CXR dataset. However, the range producing the top accuracy is sufficiently wide to confirm the robustness of the proposed approach. In order to guarantee the broadest margin, we have set the \(\eta\) parameter at the value corresponding to approximately the middle of the range, i.e., \(\eta=2\) for CT dataset and \(\eta=1.4\) for CXR dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(N_{T}\) & \(d_{m}\) & \(\sigma_{d}\) & \(TH\) & Accuracy & Precision & Recall & F-Measure \\ \hline \multicolumn{8}{c}{**Computed Tomography (\(\eta=2\))**} \\ \hline
5 & 0.0351 & 0.0911 & 0.2173 & 0.8968 & 0.8968 & 0.8968 & 0.8968 \\
10 & 0.0312 & 0.0911 & 0.2134 & 0.9880 & 0.9883 & 0.9880 & 0.9880 \\
25 & 0.0383 & 0.0852 & 0.2087 & 0.9960 & 0.9960 & 0.9960 & 0.9960 \\
50 & 0.0414 & 0.0787 & 0.1988 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
100 & 0.0460 & 0.0728 & 0.1916 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
250 & 0.0501 & 0.0704 & 0.1909 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
500 & 0.0544 & 0.0664 & 0.1872 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
3500 & 0.0732 & 0.0566 & 0.1864 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ \hline \multicolumn{8}{c}{**X-Rays (\(\eta=1.4\))**} \\ \hline
5 & 0.0022 & 0.0807 & 0.1152 & 0.7817 & 0.8474 & 0.7817 & 0.7700 \\
10 & 0.0031 & 0.0724 & 0.1045 & 0.9188 & 0.9300 & 0.9188 & 0.9188 \\
25 & 0.0076 & 0.0722 & 0.1087 & 0.9137 & 0.9262 & 0.9137 & 0.9129 \\
50 & 0.0125 & 0.0669 & 0.1062 & 0.9645 & 0.9669 & 0.9645 & 0.9644 \\
100 & 0.0179 & 0.0492 & 0.0868 & 0.9898 & 0.9901 & 0.9898 & 0.9898 \\
250 & 0.0257 & 0.0329 & 0.0718 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\
500 & 0.0339 & 0.0325 & 0.0794 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results obtained by the proposed approach at different number \(N_{T}\) of target images.