## 4 Experiments and Discussions
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Head CT**} & \multicolumn{2}{c}{**Brain CT**} & \multicolumn{2}{c}{**Hand X-ray**} \\  & **ACO** & **SACO** & **ACO** & **SACO** & **ACO** & **SACO** \\ \hline \multicolumn{8}{c}{1200 iterations} \\ Sin & 0.2694 & 0.4137 & \(-\)0.1265 & 0.0111 & 0.0346 & 0.0547 \\ KH & \(-\)0.3379 & \(-\)0.2406 & \(-\)0.1366 & 0.0648 & \(-\)0.6616 & \(-\)0.5  
Running timeThe average running time was around 4500 seconds for both ACO and SACO with the presented parameters on the utilized computer. Table 1 shows the best, maximal results of the number of correctly identified pixels standardized using the overall average, \(Avg=2107.030303\), and standard deviation, \(StdDev=563.50\), for all considered medical images, and operators on the sensitive ant colony method (SACO) and ACO denoised with DnCNN. Table 2 shows the [PERSON] [15], [PERSON], Sobel and [PERSON] methods results and Table 3 illustrates the original ACO and SACO results before post-processing with DnCNN. The best solutions obtained for the considered medical images (Head CT, Brain CT and Hand X-ray) while comparing ACO and SACO for 300,000 iterations and the considered demicontractive operators are included in Figure 2; in the last image, the original medical images are overlapped with the best solutions. AnalysisThe values are already standardized based on the denoised ACO and SACO, [PERSON], [PERSON], [PERSON], and [PERSON] results; therefore, the difference between SACO and ACO is significant from an analytic perspective. * _An operator comparison analysis_ based on Figure 3 uses the difference of SACO vs. ACO values; for the Sin-operator, the difference has a majority of negative values, with ACO obtaining better values than SACO for the considered operators (44.45%); for the other two operators, \(\chi\)-operator (Chi) and KH-operator, 77.78% shows SACO performing better than ACO on the 9 considered cases of medical images;
* _Medical image analysis_. For each medical image, including head CTs, brain CTs and hand X-rays, Figure 3 identifies operators' behavior on the difference between SACO and AC0. The lowest SACO performance, 44.44%, was obtained for the head CT medical images; its highest performance was 100% for the brain CT images, while for the hand X-rays, a 55.56% performance value was obtained. The percentage is based on the number of considered medical images. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Head CT**} & \multicolumn{2}{c}{**Brain CT**} & \multicolumn{2}{c}{**Hand X-ray**} \\  & **ACO** & **SACO** & **ACO** & **SACO** & **ACO** & **SACO** \\ \hline \multicolumn{6}{c}{1200 iterations} \\ Sin & 1.5863 & 1.5729 & 0.7123 & 0.9187 & 1.0512 & 0.9153 \\ KH & 0.6871 & 0.8029 & 0.6905 & 0.9371 & 0.1637 & 0.1637 \\ Chi & 0.8281 & 0.6335 & 0.5714 & 0.6184 & \(-\)0.0476 & 0.0799 \\ \hline \multicolumn{6}{c}{30,000 iterations} \\ Sin & 1.5981 & 1.5746 & 0.8801 & 0.9690 & 0.9187 & 0.9153 \\ KH & 0.8096 & 0.8046 & 0.8499 & 0.9354 & 0.1553 & 0.1755 \\ Chi & 0.5949 & 0.6351 & 0.5596 & 0.6200 & 0.0262 & 0.0799 \\ \hline \multicolumn{6}{c}{300,000 iterations} \\ Sin & 1.5528 & 1.5746 & 0.7777 & 0.9656 & 0.9338 & 0.9153 \\ KH & 0.7928 & 0.8046 & 0.7777 & 0.9371 & 0.1855 & 0.1755 \\ Chi & 0.6569 & 0.6335 & 0.5781 & 0.6200 & \(-\)0.0443 & 0.0799 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The best number of correctly identified number of pixels, standardized using the overall average and standard deviation for all considered medical images, with every considered operator on all considered algorithms; results for sensitive (SACO) and original ACO methods. _Stability \(\mathcal{S}\) quality of the solutions._ The quality of the partial solution is influenced by the amount of modified pheromone of the ants' trail. The stability of the global solution is influenced by used parameters. The included PSL parameter hopefully influenced the global solution for the better.