| Resumen |
In this paper, we propose the use of Alpha-Beta associative approach as an Unconventional Computing method in the prediagnosis of benign or malignant tumors of breast cancer, applying morphological operators to get a better accurate result in a simple way; trying to avoid invasive diagnostic methods like biopsies, as far as possible.
This proposal provides for the Alpha-Beta Support Vector Associative Machine created in 2008 and tested for classification of binary images, also we propose the application of mathematical morphology operators to improve the segmentation of images which are classified. The results show that the classification model to detect malignancy is very competitive compared to others of the best known classification methods, having 83.78% and 85.33% using Mathematical Morphology operators. |
