Abstract:
The automatic diagnosis of breast cancer is an important medical
problem. This paper hybridizes metaphors from cells membranes and
intercommunication between compartments with clonal selection principle
together with fuzzy logic to produce a fuzzy rule system in order to be used
in diagnosis. The fuzzy-membrane-immune algorithm suggested were implemented
and tested on the Wisconsin breast cancer diagnosis (WBCD)
problem. The developed solution scheme is compared with five previous
works based on neural networks and genetic algorithms. The algorithm
surpasses all of them. There are two motivations for using fuzzy rules with
the membrane-immune algorithm in the underline problem. The first is
attaining high classification performance. The second is the possibility of
attributing a confidence measure (degree of benignity or malignancy) to
the output diagnosis, beside the simplicity of the diagnosis system, which
means that the system is human interpretable
