Mammography is crucial for early identification and screening of breast cancer. Mammogram images can identify and classify anomalies and explain breast tissue, as well as other useful information. A technique for obtaining informative signs to lower false positives in the detection of breast tumor areas is investigated in this research work. The key problem in this investigation is differentiating the area of the breast tumor from the area of normal tissue. This issue was resolved by using the LBP approach to extract informative characters from the region of interest (ROI) in the mammography images provided in the international mini-MIAS image dataset.