## 4 Experiments and Discussions
Figure 3: Head CT, Brain CT and Hand X-ray results based on the difference between SACO and ACO standardized values, before (up) and after post-processing with the DeNoise convolutional neural network (DnCNN) (down). Figure 2: Successive illustrations of the best solutions obtained after 300,000 iterations with _Ant colony optimization (ACO)_ and _Sensitive ACO (SACO)_ post-processed with the _Denoise Convolutional Neural Network (DnCNN)_ and the overlapped best solutions’ edges over the original medical images for **(a)** Brain CT; **(b)** Head CT and **(c)** Hand X-ray. The global solution is found after the entire ant colony, based on the existing pheromone information, is guided to more promising regions in the search space. The pheromone sensitivity factor balances the exploring and exploiting activities; its value is a number from \([0,1]\). An ant ignores information when \(PSL=0\) and has the maximum pheromone sensitivity when \(PSL=1\). * _An exploring search_ is made by independent ants with a low \(PSL\) value. * _An exploiting search_ is made by sensitive ants to pheromone traces, the intensively exploitative ants with a high \(PSL\) value. In time, the process modifies ants' pheromone sensitivity (\(PSL\)). In the current work, the \(PSL\) is globally modified (increased or decreased) by the search space topology [17]. _SACO advantages_. By adding the PSL vector, the present algorithm offers the stability of its solutions; for the considered examples, after around 300 iterations, SACO generated edges which almost overlapped over the original images. As a plus, the image edge results are much more compact and close to the original when compared with the ACO results. _SACO disadvantages_. As the number of parameters increases, the user should properly configure their values. This could take more time and resources, but the improved results are worth the effort. Figure 4 shows the improvements of SACO-DnCNN compared to the Canny, Prewitt, Sobel and Roberts edge detection techniques [32]. The best SACO-DnCNN results, using \(\chi\), outperform the Canny Edge detector results by 37.76%; the Prewitt, Sobel and Roberts [32] methods were significantly outperformed by over \(159\%,157\%\) and \(224\%\). Future work will use images with higher resolutions, and hopefully the impact of sensitivity will improve the problem solutions. Future work will also include implementing specific ACO and SACO features to solve publicly available medical datasets, including COVID- and SARS-Cov-2-related data sets. Furthermore, sensitivity for different artificial intelligence models could be involved within different domains, e.g., data mining [33] and similar. Other improvements could utilize fuzzy techniques and multiple ant colonies for ACO, as in [34], which could be used to enhance the solutions for image edge detection. Further work could make use of image segmentation with edge detection in order to obtain a more thorough edge [35]. As prerequisites for the development of knowledge-based applications, ontologies for the segmentation of radiological images [36] were proposed by the authors. Other metaheuristics, mostly bio-inspired ones [37], could be further enhanced with sensitivity features in order to improve the results of complex problems. Human-in-the-loop [38] could also enhance the problem results.