An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network
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 × 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 DBN-based CAD system for use as in the field of breast cancer diagnosis.
KeywordsBreast cancer classification Digital mammography Computer-aided diagnosis (CAD) Automatic mass detection Deep learning Deep belief network (DBN)
This work was supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as Global Frontier Project (CISS- 2011-0031863). This work was supported by International Collaborative Research and Development Programme (funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (N0002252)
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Conflict of interest
The authors declare that there is no conflict of interest regarding the publication of this paper.
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