New developments in the diagnostic procedures to reduce prospective biopsies breast

This paper studies the computer-aided diagnosis technique potential in discriminating accurately benign masses among a given subset of 100 patients which makes it possible to degrade cases from Breast Imaging-Reporting and Data System (BIRADS) 3 to BIRADS 2 avoiding prospective biopsies. Such accuracy is required since expert radiologists assign BIRADS3 category by default mostly for reducing false negative cases. We aim here at classifying masses on a risk rate scale for malignancy. The proposed system segments automatically potential masses and quantifies critical related features. A decision tree was accordingly applied. In a first level, a mass detection is based on a new local pattern model named Weighted Gray Level and Local Difference features (WGLLD) and a nearest neighborhood (NN) a classifier. In the second level, Zernike moment features were used for shape characterization with connection by an Artificial Neural Network (ANN) based classifier, after that we segment masses and extract shape features using Zernike moments. For validation purposes, a total of 100 lesions from local breast database (FDDSM)is used. Most of these cases are biopsy confirmed. The system successfully downgraded 7 cases over 41 rated by the expert as belonging to BIRADS 3 to BIRADS 2, but, it recommended biopsy for 41/100 atypical lesions. Ultimately, the system identified 59 benign lesions to BIRADS 2, 7 cases from these were classified as belonging to BIRADS 3 by the expert, and thus reached a reduction of unnecessary breast biopsies. The proposed CAD system allows a classification rate of 98% (only one benign case is missed). The proposed Computer Aided Diagnosis (CAD) system demonstrated the ability to predict benignancy of the most difficult cases.30 Appearance changes were also shown to be more characterizing after mammogram enhancement. With further validation, these results could form a substrate for a clinically useful computer-aided diagnosis tool which could provide earlier detection of breast cancer signs.