Abstract
<p>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 <em>et al.</em>, 2001. Moreover, the model improved 8% for normal/abnormal classification over Subashimi <em>et al.</em>, 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.</p>
Highlights
Breast Cancer is the most significant health problem in the world and early diagnosis has a major role in its treatment
It has been shown that early detection and treatment of breast cancer are the most effective methods of reducing mortality [2]
Developing a Computer Assisted Detection (CAD) algorithm, which uses features extracted from the breast profile region; the region of interest (ROI) is vital
Summary
Breast Cancer is the most significant health problem in the world and early diagnosis has a major role in its treatment. It has been shown that early detection and treatment of breast cancer are the most effective methods of reducing mortality [2]. The most effective method for early detection and screening of breast cancers is mammography [3]. The estimated sensitivity of radiologists in breast cancer screening is only about 75% [5]. Developing a CAD algorithm, which uses features extracted from the breast profile region; the region of interest (ROI) is vital. This will cause a reduction in the number of unneeded biopsies to patients with benign masses, and reduces healthcare cost and avoids putting the patient in mental and physical stress [7]
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