This paper intends to develop a novel breast cancer detection model for classifying the normal, benign or malignant patterns in a mammogram. The diagnosis process is done based on three stages such as pre-processing, feature extraction and classification. Initially, the Discrete Fourier Transform (DFT) is applied in the processing stage. Next, to pre-processing, the Gray Level Co-Occurrence Matrix (GLCM) features of the image are extracted. The GLCM-based features are then classified using Support Vector Machine (SVM) for classifying the mammogram. Further, the weights of the SVM are optimized using the Grey Wolf optimization (GWO) model for improving the classification accuracy. This classification mechanism is used to diagnose the benign and malignant patterns in a mammogram. Moreover, the proposed scheme is evaluated over traditional models such as GA, PSO and FF as well as the outcomes is verified.
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