Abstract
Breast cancer is a leading disease worldwide for the cause of women's death, among other conditions such as tuberculosis and malaria. The early stage of breast cancer saves the life of millions of women's worldwide. The computer-aided diagnosis (CAD) of breast cancer detection is better effective tools. The performance of CAD based on the process of feature selection of breast imagery and applied algorithm for the detection and classification of symptoms. This paper proposed feature optimization-based breast cancer detection using a glowworm optimization algorithm. For the classification of cancer cell applied pulse coupled neural network model. The pulse coupled neural network model is an excellent advantage over the conventional neural network model. The proposed algorithm test on MATLAB environments with the reputed dataset of breast cancer, CBIS-DDSM.
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