Breast cancer is one of the most commonly occurring cancers among women globally. The accurate detection and classification of the abnormalities such as masses and microcalcifications in mammograms is a challenging task for the radiologist without which the survival rate of the breast cancer patients may increase worldwide. This paper presents a novel Computer Aided Diagnosis (CAD) system which uses Cellular Neural Network (CNN) technique, which is optimized using Particle Swarm Optimization (PSO) for detection and Particle Swarm Optimised Probabilistic Neural Network (PSOPNN) for the classification of breast masses as benign or malignant. The breast mass texture feature extraction is carried out using Gray Level Co-occurrence Matrix (GLCM) and the optimal texture features are selected using a particle swarm optimized feature selection. The performance of the proposed system can be evaluated using the True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN) values.
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