Digital mammogram classification using 2D-BDWT and GLCM features with FOA-based feature selection approach - Mohanty, Figlu; Rup, Suvendu; Dash, Bodhisattva; Majhi, Banshidhar; Swamy, M. N. S. (Neural computing & applications, 2020)
This paper proposes an improved computer-aided diagnosis model to identify mammographic images as normal or abnormal, and further, benign or malignant. The proposed scheme employs all the steps associated with other classification schemes; however, the contribution of the suggested scheme is fourfold. Initially, a fusion-based feature extraction method is employed to obtain the features using a combination of 2-D block discrete wavelet transform (2D-BDWT) and gray-level co-occurrence matrix (GLCM). Next, principal component analysis (PCA) is utilized to reduce the large dimension of the feature vector. Furthermore, to select the most optimal features from the reduced set of features, forest optimization algorithm (FOA) is used. The FOA-based feature selection algorithm is utilized as a wrapper-based technique which includes both feature selection and classification. In the proposed framework, several classifiers, namely support vector machine (SVM), k-nearest neighbor (k-NN), and decision tree (C4.5), are applied. The proposed method is compared with the benchmark schemes on two standard datasets, namely Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM). Simulation results and analysis confirm that the proposed scheme brings potential improvements with respect to classification accuracy, sensitivity, specificity, area under curve, F -score, and Matthews correlation coefficient. The classification accuracy is measured with respect to normal versus abnormal and further, benign versus malignant. The proposed scheme with different combinations of classifiers, namely 2D-BDWT + GLCM + PCA + FOA + SVM, 2D-BDWT + GLCM + PCA + FOA + k-NN, and 2D-BDWT + GLCM + PCA + FOA + C4.5, achieves a maximum classification accuracy of 100% for both the MIAS and DDSM datasets.