An efficient approach for classification of mammograms for detection of breast cancer is presented. The approach utilises the two-dimensional discrete orthonormal S-transform (DOST) to extract the coefficients from the digital mammograms. A feature selection algorithm based the on null-hypothesis test with statistical 'two-sample t-test' method has been suggested to select most significant coefficients from a large number of DOST coefficients. The selected coefficients are used as features in the classification of mammographic images as benign or malignant. This scheme utilises an AdaBoost algorithm with random forest as its base classifier. Two standard databases Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) are used for the validation of the proposed scheme. Simulation results show an optimal classification performance with respect to accuracies of 98.3 and 98.8% and AUC (receiver operating characteristic) values of 0.9985 and 0.9992 for MIAS and DDSM, respectively. Comparative analysis shows that the proposed scheme outperforms its competent schemes.
This paper presents an effective scheme to identify the abnormal mammograms in order to detect the breast cancer. The scheme utilizes the segmentation-based fractal texture analysis (SFTA) method to extract the textural features from the mammograms for the classification of normal and abnormal mammograms. A fractal analysis has been applied to collect the qualitative information of textural features. A fast correlation-based filter (FCBF) method has been used to select feature subsets containing significant features, which are used for classification purpose. The scheme was tested on the mammogram images of MIAS database. In this paper, support vector machine (SVM) has been utilized for classification of mammograms. Simulation results show an optimal classification performance index as the area under the curve (AU C) of 0.9831 in the ROC analysis.
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