• Alaa M. Adel El-shazli Department of Computer Engineering, Arab Academy for Science and Technology, Alexandria, Egypt
  • Sherin M. Youssef
  • Marwa Elshennawy



Breast Cancer, Classification, Pre-Processing, Segmentation, Sensitivity, Specificity


The objective of this research was to introduce a new system for automated detection of breast masses in mammography images. The system will be able to discriminate if the image has a mass or not, as well as benign and malignant masses. The new automated ROI segmentation model, where a profiling model integrated with a new iterative growing region scheme has been proposed. The ROI region segmentation is integrated with both statistical and texture feature extraction and selection to discriminate suspected regions effectively. A classifier model is designed using linear fisher classifier for suspected region identification. To check the system's performance, a large mammogram database has been used for experimental analysis. Sensitivity, specificity, and accuracy have been used as performance measures. In this study, the methods yielded an accuracy of 93% for normal/abnormal classification and a 79% accuracy for bening/malignant classification. The proposed model had an improvement of 8% for normal/abnormal classification, and a 7% improvement for benign/malignant classification over Naga et al., 2001. Moreover, the model improved 8% for normal/abnormal classification over Subashimi et al., 2015. The early diagnosis of this disease has a major role in its treatment. Thus the use of computer systems as a detection tool could be viewed as essential to helping with this disease.


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How to Cite

El-shazli, A. M. A., S. M. Youssef, and M. Elshennawy. “COMPUTER-AIDED MODEL FOR BREAST CANCER DETECTION IN MAMMOGRAMS”. International Journal of Pharmacy and Pharmaceutical Sciences, vol. 8, no. 2, Sept. 2016, pp. 31-34, doi:10.22159/ijpps.2016v8s2.15216.



Original Article(s)