Abstract
Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC.
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Whang, J. S., Baker, S. R., Patel, R., Luk, L., and Castro, III, A., The causes of medical malpractice suits against radiologists in the United States. Radiology 266(2):548–554, 2013.
Glorot, X., and Bengio, Y., Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249–256, 2010.
Tang, J., Rangayyan, R. M., Xu, J., El Naqa, I., and Yang, Y., Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances. IEEE Trans. Inf. Technol. Biomed. 13(2):236–251, 2009.
Wei, J., Sahiner, B., Hadjiiski, L. M., Chan, H. P., Petrick, N., Helvie, M. A., Roubidoux, M. A., Ge, J., and Zhou, C., Computer-aided detection of breast masses on full field digital mammograms. Med. Phys. 32(9):2827–2838, 2005.
Wei, L., Yang, Y., Nishikawa, R. M., and Jiang, Y., A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications. IEEE Trans. Med. Imaging 24(3):371–380, 2005.
Zhang, W., Doi, K., Giger, M. L., Wu, Y., Nishikawa, R. M., and Schmidt, R. A., Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Med. Phys. 21(4):517–524, 1994.
Wu, Y., Giger, M. L., Doi, K., Vyborny, C. J., Schmidt, R. A., and Metz, C. E., Artificial neural networks in mammography: Application to decision making in the diagnosis of breast cancer. Radiology 187(1):81–87, 1993.
Oliver, A. et al., Automatic microcalcification and cluster detection for digital and digitised mammograms. Knowledge-Based Systems 28:68–75, 2012.
LeCun, Y., Bengio, Y. et al., Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks 3361(10):1995, 1995.
LeCun, Y., Kavukcuoglu, K., Farabet, C., et al., Convolutional networks and applications in vision. In: ISCAS, vol. 2010, pp. 253–256, 2010.
Hafemann, L. G., Oliveira, L. S., Cavalin, P., Forest species recognition using deep convolutional neural networks. In: Pattern Recognition (ICPR), 2014 22nd International Conference on, pp. 1103–1107, 2014.
Chougrad, H., Zouaki, H., and Alheyane, O., Deep convolutional neural networks for breast cancer screening. Comput. Methods Prog. Biomed. 157:19–30, 2018.
Arevalo, J., González, F. A., Ramos-Pollán, R., Oliveira, J. L., and Lopez, M. A. G., Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Prog. Biomed. 127:248–257, 2016.
Jiao, Z., Gao, X., Wang, Y., and Li, J., A deep feature based framework for breast masses classification. Neurocomputing 197:221–231, 2016.
Yu, F., Koltun, V., Multi-scale context aggregation by dilated convolutions. arXiv Prepr. arXiv1511.07122, 2015.
Suckling, J., et al. Mammographic Image Analysis Society (MIAS) database v1. 21, 2015.
Springenberg, J. T., Dosovitskiy, A., Brox, T., Riedmiller, M., Striving for simplicity: The all convolutional net. arXiv Prepr. arXiv1412.6806, 2014.
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H., Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv Prepr. arXiv1802.02611, 2018.
Eklund, G. W., Cardenosa, G., and Parsons, W., Assessing adequacy of mammographic image quality. Radiology 190(2):297–307, 1994.
Anitha, J., and Peter, J. D., Mammogram segmentation using maximal cell strength updation in cellular automata. Med. Biol. Eng. Comput. 53(8):737–749, 2015.
Adams, R., and Bischof, L., Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6):641–647, 1994.
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., Whyntie, T., Nachev, P., Modat, M., Barratt, D. C., Ourselin, S., Cardoso, M. J., and Vercauteren, T., NiftyNet: A deep-learning platform for medical imaging. Comput. Methods Prog. Biomed. 158:113–122, 2018.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A., Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929, 2016.
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Agnes, S.A., Anitha, J., Pandian, S.I.A. et al. Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN). J Med Syst 44, 30 (2020). https://doi.org/10.1007/s10916-019-1494-z
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DOI: https://doi.org/10.1007/s10916-019-1494-z