Haralick Feature-Based Texture Analysis from GLCM and SRDM for Breast Cancer Detection in Mammogram Images
V. Jeevitha, I. Laurence Aroquiaraj · 2025
Mammogram images utilizing the breast cancer identification with radiologist finding the experiment to the X-ray specification with analysis the data to represents the features and also X-ray is low radiation it no causing the skin and tissue damages and very safe tools to taken the health care fields. The main methodology of GLCM and SRDM to proposed the mammogram image with angular direction and distance used the ranges from d=2 and direction as for θ = 0°, 45°, 90°, 135° to finding the best results performance evaluation of haralick features are very well metrics to finding the features. The objective of mammogram images as irrelevant features avoided to best features selected to the classification with normalize the features to the best performance of metrics. The database utilizing with mammogram image analysis society (MIAS) database categories as benign, malignant and normal from that data implementing the feature extraction methods of GLCM. The result shows that the comparison of GLCM and SRDM features are efficient and achieved the maximum higher performance metrics of GLCM is best outcomes of haralick features like "contrast", "dissimilarity", "homogeneity", "ASM", "energy", "correlation", "variance", "entropy", "sum average", "sum variance", "sum entropy", "difference average", "difference variance and difference entropy" as result experimental analytics of performance metrics.