An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network
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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.
- 8.Al-antari, M., AL-masni, M., & Kadah, Y. (2017). Hybrid model of computer-aided breast cancer diagnosis from digital mammograms. Journal of Scientific and Engineering Research, 4(2), 114–126.Google Scholar
- 10.Al-Olfe, M., Al-Akwaa, F., Mohamed, W., & Kadah, Y. (2010). Computer-aided diagnosis of digital mammography images using unsupervised clustering and biclustering techniques. Proceedings SPIE conference on Medical Imaging, 7624, 1–6.Google Scholar
- 11.Kang, Y., Ke, L., & Mu, N. (2010). Mass computer-aided diagnosis method in mammogram based on texture features. In Proceedings of IEEE BMEI Conference on biomedical engineering and informatics (BMEI) (vol. 1, pp. 354–357).Google Scholar
- 15.Kooi, T., Gubern A., Mordang J., Mann R., Pijnappel R., Schuur K, Heeten, A., & Karssemeijer N. (2016). A comparison between a deep convolutional neural network and radiologists for classifying regions of interest in mammography. In A. Tingberg et al. (Eds.), IWDM 2016, LNCS 9699 (pp. 51–56). Switzerland: Springer.Google Scholar
- 17.Heath, M., Bowyer, K., Kopans, D., Moore, R., & Kegelmeyer, W. (2001). The digital database for screening mammography. In Proceedings of the 5th international workshop on digital mammography (pp. 212–218).Google Scholar
- 19.Wu, T., Moore, R., & Kopans, D. (2010). Multi-threshold peripheral equalization method and apparatus for digital mammography and breast Tomosynthesis. Google patents, US Patent 7, 764, 823.Google Scholar
- 20.Gonzalez, R., & Woods, R. (2008). Digital image processing (3rd ed.). USA: Prentice Hall. ISBN 013168728.Google Scholar
- 28.Kumar, I., Virmani, J., & Bhadauria, H. (2015). A Review of Breast Density Classification Methods. In Proceedings of IEEE international conference on computing for sustainable global development (INDIACom) (pp. 1960–1967).Google Scholar
- 33.Duin, R., Juszczak, P., Paclik, P., Pekalska, E., Ridder, D., Tax, D., & Verzakov, S. (2007). PRTools4.1: A Matlab toolbox for pattern recognition. Delft: Delft University of Technology.Google Scholar
- 35.Thomas, M. C. (1965). Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 14(3), 326–334.Google Scholar
- 37.Nam, S., Park, S., Park, J., & Kim, T-S. (2015). A single depth sensor based human activity recognition via deep belief network. In Proceedings of WCSET of the 4th world conference on applied sciences, engineering & technology (pp. 15–19).Google Scholar
- 40.Mavroforakis, M., Georgiou, H., Dimitropoulos, N., Cavouras, D., & Theodoridis, S. (2006). Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artificial Intelligence in Medicine, 37(2), 145–162.CrossRefGoogle Scholar
- 42.Tome, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M., & Tubaro, S. (2016). Deep convolutional neural networks for pedestrian detection. Signal Processing: Image Communication. doi: 10.1016/j.image.2016.05.007.