Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer

In this paper, we propose a mammogram classification scheme to classify the breast tissues as normal, benign or malignant. Feature matrix is generated using GLCM to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. To derive the relevant features from the feature matrix, we take the help of t-test and F-test separately. The relevant features are used in a BPNN classifier for classification. Two standard databases MIAS and DDSM are used for the validation of the proposed scheme. It is observed that t-test based relevant features outperforms to that of F-test with respect to accuracy. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy and area under curve (AUC) of receiver operating characteristic (ROC). The accuracy measures are computed with respect to normal vs. abnormal and benign vs. malignant. For MIAS database these accuracy measures are 98.0% and 94.2% respectively, whereas for DDSM database they are 98.8% and 97.4%.