Effect of Fuzzy Genetic Artificial Neural Networks Agorithm Extracting Microcalcification
Guangyu Zhang · Journal of Sun Yat-sen University · 2008
[Objective] Microcalcification is one of the most important characteristics of early breast tumors. In this paper, we proposed a new method of microcalcification detection by integrating genetic algorithm, fuzzy mathematics and artificial neural networks. The method could provide preprocessing for automatic recognition of breast cancers, and assist doctors to diagnose early breast cancer.[Methods] A lot of random training samples were firstly produced; then, these samples were classified into the background and microcalcifications using the fuzzy genetic method. Finally, the 310 regions of interest were classified into the background and microcalcifications using the trained neural networks.[Results] Compared with similar literature about microcalcification detection, we obtained better positive detection ratio with the same false detection ratio.[Conclusions] Experimental results demonstrate that our method obtain better extraction effect by integrating genetic algorithm, fuzzy mathematics and neural network, compared with the method simply using artificial neural network.