Breast cancer is a current problem that causes the death of many women. In this work, we test meta-heuristics applied to the segmentation of mammographic images. Traditionally, the application of these algorithms has a direct relationship with optimization problems; however, in this study, its implementation is oriented to the segmentation of mammograms using the Dunn index as an optimization function, and the grey levels to represent each individual. The update of grey levels during the process results in the maximization of the Dunn's index function; the higher the index, the better the segmentation will be. The results showed a lower error rate using these meta-heuristics for segmentation compared to a well-adopted classical approach known as the Otsu method.

Early breast cancer diagnosis is crucial, as it can prevent further complications and save the life of the patient by treating the disease at its most curable stage. In this paper, we propose a new artificial immune system model for associative classification with competitive performance for breast cancer detection. The proposed model has its foundations in the biological immune system; it mimics the detection skills of the immune system to provide correct identification of antigens. The Wilcoxon test was used to identify the statistically significant differences between our proposal and other classification algorithms based on the same bio-inspired model. These statistical tests evidenced the enhanced performance shown by the proposed model by outperforming other immune-based algorithms. The proposed model proved to be competitive with respect to other well-known classification models. In addition, the model benefits from a low computational cost. The success of this model for classification tasks shows that swarm intelligence is useful for this kind of problem, and that it is not limited to optimization tasks.

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Segmentation is one of the main tasks related to breast cancer classification. Automatic and semiautomatic algorithms have been proposed lately, and in this paper, a new method to segment mammography images is proposed using novel bat algorithm (NBA) and unsupervised metric measures as objective function. Results showed a useful method to segment mammographies using bioinspired algorithms based on bat optimization.

The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification.