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About every minute a woman dies out of breast cancer, worldwide. The need for early detection cannot be overstated. Towards this, mammography is a boon for both early detection and screening of breast cancer tumors. It is an imaging system that uses low dose x-rays for examining the breasts, by the electrons reflected from the tissues. The use of screening mammography is associated with the detection of breast cancer at an earlier stage and smaller size, resulting in a reduction in mortality. This study was aimed at enhancing the current accuracy (diagnostic) of digital mammograms using industry standard simulation software tool, MATLAB and the MIAS dataset. The technique involves identification of tumor cells to segment them in terms of different stages of the disease. We consider the process of object detection, recognition and classification of mammograms with the aim of differentiating between normal and abnormal (benign or cancerous) cells. It is reported that dense breasts can make traditional mammograms more difficult to interpret. Although newer digital mammography techniques claim for better detection in dense breast tissues, the availability of such expensive digital mammograms is not widespread. This problem can be minimized by analyzing different breast structures (mammograms) using the MATLAB numerical analysis software for image processing applications. The results indicated up to 91% accuracy, compared to 70% at present. Our proposed solution has proved to be an effective way of detecting breast cancer early in different types of breast tissues.