An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network

作者：Al antari Mugahed A; Al masni Mohammed A; Park Sung Un; Park JunHyeok; Metwally Mohamed K; Kadah Yasser M; Han Seung Moo; Kim Tae Seong^*

来源：Journal of Medical and Biological Engineering, 2018, 38(3): 443-456.

DOI：10.1007/s40846-017-0321-6

• Breast cancer classification
• Digital mammography
• Computer-aided diagnosis (CAD)
• Automatic mass detection
• Deep learning
• Deep belief network (DBN)

Computer-aided diagnosis (CAD) offers assistance to radiologists in the interpretation of medical images. A CAD system learns the nature of different tissues and uses this information to diagnose abnormalities. In this paper, we propose a CAD system for breast cancer diagnosis via deep belief network (DBN) that automatically detects breast mass regions and recognizes them as normal, benign, or malignant. In this study, we utilize a standard digital database of mammography to evaluate our proposed DBN-based CAD system for breast cancer diagnosis. We utilize two techniques of ROI extraction: multiple mass regions of interest (ROIs) and whole mass ROIs. In the former technique, we randomly extract four ROIs with a size of 32 x 32 pixels from a detected mass. In the latter technique, the whole detected breast mass is utilized. A total of 347 statistical features are extracted for both techniques to train and test our proposed CAD system. For classification, we utilized linear discriminant analysis, quadratic discriminant analysis, and neural network classifiers as the conventional techniques. Finally, we employed DBN and compared the results. Our results demonstrate that the proposed DBN outperforms the conventional classifiers. The overall accuracies of a DBN are 92.86% and 90.84% for the two ROI techniques, respectively. The presented work shows the feasibility of a DBNbased CAD system for use as in the held of breast cancer diagnosis.

• 出版日期2018-6