Artificial Immune Systems for Identifying Malignant Breast Cancer

Ramin Javadzadeh


Cancer is a gene disruption based disease in which, body’s cells behave abnormally. These cancerous cells start uncontrolled reproduction and usually offend organisms, and a mass of cancerous cells is called tumor. Epidemiological studies have revealed that breast cancer is the leading cause of mortality among female population. Owing to the essence of natural lipid masses in breast, identification of tumor type from mammographic images is challenging. In this sense, almost patients are supposed to have biopsy operation which might lead wide variety of side effects. On this basis, a novel expert system based on negative selection and fuzzy sets is proposed to save assistantship functionality for specialists and physicians. In some sense, multiple pattern repositories are injected into the infrastructure of artificial immune systems. To verify and validate the proposed approach, several computational simulations have been performed. Simulation results prove that proposed method provide more accurate results in identifying malignant tumors in comparison to traditional pattern recognition techniques as such artificial neural networks and neuro-fuzzy systems.


Artificial Immune Systems; Breast Cancer; Pattern Recognition; Classification; E-Health; E-Medicine; Data-Mining

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