Current Proceedings on Technology

Yazarlar: Somaiyeh Dehghan

Konular:-

Anahtar Kelimeler:Breast cancer,Classification,CMAC neural network model,Datamining

Özet: Breast cancer is the most common and the second most fatal cancer for women. The most effective way to reduce breast cancer deaths is detect it earlier. Nowadays, Fine Needle Aspiration (FNA) is regarded as a practical procedure to diagnose breast cancer. The characteristics of cell nuclei and contextual features serve as the major indicators. The researchers have designed different kinds of classifiers in order to diagnosis breast cancer. Based on the analysis done, CMAC neural network showed faster learning in high dimensional problems, but the main challenge in classification is how to deal with high dimensional attribute space which increase the memory required by CMAC neural network. In the present paper a New-CMAC neural network model is proposed for classification of breast Cancer into benign and malignant category which requires less memory. The results reveal that the proposed model is more useful than any other algorithms and has least training time with respect to other ANNs algorithms. 

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