Abstract
Breast cancer is the most identified reason for death among women worldwide. New developments in the field of biomedical image processing have enabled the early and effective diagnosis of breast cancer. Therefore, this article aims at developing an effective computer-aided diagnosis (CAD) system which can precisely label the mammograms as normal, benign or malignant. In the presented scheme, compound local binary pattern (CLBP) is used to obtain the texture features from the extracted regions of interest (ROI) of mammograms. Then, principal component analysis (PCA) is used to obtain the reduced feature set. Finally, different classifiers like support vector machine (SVM), k-nearest neighbors (KNN), C4.5, artificial neural network (ANN), and Naive Bayes are utilized for classification. The proposed model is validated on two standard datasets, namely, MIAS and DDSM. Further, the proposed model’s performance is assessed in terms of different measures like classification accuracy, sensitivity, and specificity. From the result analysis, it is noticed that the proposed scheme achieves better classification accuracy as compared to the benchmark schemes.
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Bagchi, M.J., Mohanty, F., Rup, S., Dash, B., Majhi, B. (2018). Digital Mammogram Classification Using Compound Local Binary Pattern Features with Principal Component Analysis Based Feature Reduction Approach. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-13-1810-8_27
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DOI: https://doi.org/10.1007/978-981-13-1810-8_27
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