## 5 Discussion and Conclusions  
The meta-heuristic-based segmentations showed a lower error compared to the Otsu method, which is relevant since the Otsu method is one of the classic and still used methods for thresholding. These algorithms showed better performances than the Otsu method, even when using them without testing different configurations. When contrasting the meta-heuristic with the highest error (SA) and the Otsu method, it can be observed that the error presented in simulated annealing is lower than that of Otsu, which represents an important fact since Otsu is a well-known and useful method for thresholding. According to the runtime test, it was observed that the Otsu method had the lowest run time. However, implementing this method with the Matlab(tm) software resulted in an optimized and reviewed version of the algorithm. Implementing the meta-heuristic algorithms was a dummy one, with no use of parallel programming. Many of the meta-heuristics have numerous parameters, which increases the performances of the algorithm depending on the values for each parameter. In this work, we change no parameters for such algorithms, and we did not prove other configurations, but in future work, this could be improved. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **SVM** & **RIPPER** & **AIS** \\ \hline BCDR & **80.111** & 75.414 & 64.360 \\ BCWO & 96.996 & 95.565 & **97.420** \\ BCWP & 76.263 & 77.778 & **79.290** \\ LCDS & 56.250 & 50.000 & **68.750** \\ MMDS & 79.501 & **82.934** & 78.980 \\ HSDS & 72.876 & 73.856 & **76.471** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification performances. The classification model presented (AIS) showed a good performance in most of the datasets where it was tested. This is relevant since it is a new intelligent computing algorithm that opens a research gap in bio-inspired algorithms. The proposed method can be used as a guide for the radiologists when there is a high demand for processing mammograms and it can also be used as a second opinion in critical cases. In future work, we can implement preprocessing for enhancing the contrast of the images, to compare the use of preprocessing before segmenting using the meta-heuristics. This could help the segmentation algorithms to perform a better segmentation. This could help the segmentation algorithms to perform a better segmentation. Concerning the classification algorithm, it is convenient to explore the use of mutation strategies, the global optima, and to improve the search for the desired solution. Conceptualization, A.-J.-A.-C., D.G.-P., Y.V.-R., and F.K. ; Methodology, A.-J.-A.-C., D.G.-P., Y.V.-R., and F.K. ; Software, Y.V.-R., D.G.-P.; Validation, Y.V.-R., D.G.-P.; Formal Analysis, A.-J.-A.-C., D.G.-P., Y.V.-R.; Investigation, A.-J.-A.-C., D.G.-P., Y.V.-R., and F.K. ; Resources, ; Data Curation, A.-J.-A.-C. and Y.V.-R.; Writing--Original Draft Preparation, D.G.-P., and Y.V.-R.; Writing--Review and Editing, A.-J.-A.-C., D.G.-P., and Y.V.-R.; Visualisation, A.-J.-A.-C. and Y.V.-R.; Supervision, A.-J.-A.-C.; Project Administration, A.-J.-A.-C. and D.G.-P. The authors of the present paper would like to thank the following institutions for their economic support to develop this work: Comision de Operacion y Fomento de Actividades Academicas del Instituto Politecrico Nacional (COFAA-IPN), Centro de Investigacion en Computacion (Centro de Investigacion en Computacion-IPN), Centro de Innovacion y Desarrollo Tecnologico en Computacion (CIDETEC), Escuela Superior de Ingenieria Mecanica y Electrica (ESIME), and Consejo Nacional de Ciencia y Tecnologia (CONACYT).