Texture Analysis of Breast Cancer via LBP, HOG, and GLCM techniques

Abstract Breast cancer is a prevailing reason for death, and it is a particular kind of tumor that is popular among ladies across the world. Till presently, there is no efficient method to stop the appearance of the breast tumor. Accordingly, early detection is the first stage in the diagnosis of breast tumors and reduces mortality. Screening Mammography is the most effective technique for early detection of breast tumors. Great experience and large practices of specialists are wanted when examining breast tissue in a mammogram. In this work, feature extraction techniques are offered as methods to decrease false-positive that occur in breast diagnosis. Mini-MIAS database used to evaluate these approaches. LBP, HOG, and GLCM are feature extraction techniques used for analyzing mass tissue and extract features from the ROI. Contrast, energy, correlation, and homogeneity are used as features properties. These features utilized as the input to the different classifiers which achieved the best results. To improve the diagnosis ability, “contrast limited adaptive histogram equalization” utilized as a preprocessing system. The best results gained in this work by LBP method and logistic regression classifier at ROI (30×30) where the accuracy 92.5%. The HOG method achieved the best results with the SVM classifier where accuracy 90% at ROI (30×30). GLCM provides the best results with the KNN classifier where the accuracy 89.3% at ROI (30×30). The greatest accuracy reached in the case of ROI (30×30) in all feature extraction methods that used in this work.