  SOM is a type of unsupervised learning where the goal is to discover some
underlying structure of the data. In this paper, a new extraction method based
on the main idea of Concurrent Self-Organizing Maps (CSOM), representing a
winner-takes-all collection of small SOM networks is proposed. Each SOM of the
system is trained individually to provide best results for one class only. The
experiments confirm that the proposed features based CSOM is capable to
represent image content better than extracted features based on a single big
SOM and these proposed features improve the final decision of the CAD.
Experiments held on Mammographic Image Analysis Society (MIAS) dataset.
