ABSTRACT
Breast cancer is one of the most common cancers affecting women lives worldwide. It is usually quite difficult for radiologists to accurately distinguish between malignant and benign tumor in digital mammograms. An intelligent classifier based on conventional machine learning algorithms can help radiologists classifying abnormal breast mass and diagnosing breast cancer. Recently, deep learning has attracted much research attention in medical image analysis due to its higher classifying accuracy and the capability of learning features from annotated imaging data automatically. Therefore, we proposed a deep neural network model to classify benign and malignant tumors in digital mammograms. Our model is an improved version of the AlexNet, which is a Convolutional Neural Networks (CNN) model of deep learning. Totally 115 regions of interest (ROIs) were extracted from Mammographic Images Analysis Society (MIAS) database and finally augmented to 4600 images used as the training and testing dataset. In order to compare our proposed model with conventional learning models, an SVM-based classifier was implemented based on the same dataset. Experimental results showed that our model has more significant classification capability with the accuracy of 97.57%, while the SVM-based model has only 86.08% accuracy.
- Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, Thun MJ. Cancer statistics. CA Cancer J Clin, 58(2):71--96, 2008.Google ScholarCross Ref
- GigerML, PritzkerA. Medical imaging and computers in the diagnosis of breast cancer. In: SPIE optical engineering + applications. International Society for Optics and Photonics, p 908--918, 2014.Google Scholar
- A. Jalalian, S. B. T. Mashohor, H. R. Mahmud, M. I. B. Saripan, A. R. B. Ramli, and B. Karasfi, "Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review," Clinical Imaging, vol. 37, no. 3, pp. 420--426, 2013.Google ScholarCross Ref
- Krishnan, M. et al. Statistical analysis of mammographic features and its classification using support vector machine. Expert Systems with Applications 37(1), 470--478 (2010). Google ScholarDigital Library
- Akay, M. F. Support vector machines combined with feature selection for breast cancer diagnosis. Expert systems with applications 36(2), 3240--3247 (2009). Google ScholarDigital Library
- Sahan, S., Polat, K., Kodaz, H. & Güneş, S. A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput BiolMed 37(3), 415--423 (2007). Google ScholarDigital Library
- Pérez, N. et al. Improving the performance of machine learning classifiers for Breast Cancer diagnosis based on feature selection. Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on. IEEE.209--217 (2014).Google Scholar
- J. Lesniak, R. Hupse, M. Kallenberg et al., "Computer aided detection of breast masses in mammography using support vector machine classification," in Proceedings of the Medical Imaging 2011: Computer-Aided Diagnosis, 2011.Google Scholar
- M.A. Mazurowski, J. Y. Lo, B. P. Harrawood, and G.D.Tourassi, "Mutual information-based template matching scheme for detection of breastmasses: frommammography to digital breast tomosynthesis," Journal of Biomedical Informatics, vol. 44, no. 5, pp. 815--823, 2011. Google ScholarDigital Library
- J. A. Jasmine, A. Govardhan, and S. Baskaran, "Microcalcification detection in digital mammograms based on wavelet analysis and neural networks," in Proceedings of the International Conference on Control, Automation, Communication and Energy Conservation (INCACEC '09), pp. 1--6, Perundurai, India, June 2009.Google Scholar
- M. Elter and E. Halmeyer, "A knowledge-based approach to the CADx of mammographic masses," in Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, vol. 6915 of Proceedings of SPIE, San Diego, Calif, USA, February 2008.Google Scholar
- G. Vani, R. Savitha, and N. Sundararajan, "Classification of abnormalities in digitized mammograms using extreme learning machine," in Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV '10), pp. 2114--2117, IEEE, Singapore, December 2010.Google Scholar
- LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436--444, 2015.Google ScholarCross Ref
- M. I. Razzak, S. Naz, and A. Zaib, "Deep Learning for Medical Image Processing: Overview, Challenges and Future," arXiv preprint arXiv:1704.06825, 2017.Google Scholar
- D. Shen, G. Wu, and H.-I. Suk, "Deep learning in medical image analysis," Annual Review of Biomedical Engineering, 2017.Google ScholarCross Ref
- J. Arevalo, F. A. Gonzalez, R. Ramos-Pollan, J. L. Oliveira, and M. A. Guevara Lopez, "Convolutional neural networks for mammography mass lesion classification," in Proceedings of the Engineering in Medicine and Biology Society (EMBC '15), vol. 25, pp. 797--800, August 2015.Google Scholar
- Suzuki S, Zhang X, Homma N, et al. Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. IEEE SICE; 2016: 1382--1386.Google Scholar
- Wang D, Khosla A, Gargeya R, et al. Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718, 2016.Google Scholar
- Gallego J, Montoya D, Quintero O. Detection and diagnosis of breast tumors using deep convolutional neural networks. Conference Proceedings of the XVII Latin American Conference on Automatic Control; 2016: 11--17.Google Scholar
- adoon, M. Mohsin; Zhang, Qianni; Haq, Ihsan Ul; Butt, Sharjeel; Jadoon, Adeel; Cai, Yudong. Three-Class Mammogram Classification Based on Descriptive CNN Features BioMed Research International, 2017, Vol.2017, 11 pagesGoogle Scholar
- Mohamed, Aly A.; Berg, Wendie A.; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C.; Wu, Shandong. A deep learning method for classifying mammographic breast density categories. Medical Physics, January 2018, Vol.45(1), pp.314--321Google ScholarCross Ref
- Dubrovina et al.presented a novel supervised CNN framework for breast anatomy (i.e., pectoral muscle, dense tissue, and nipple) classification in mammography images, using a patch-wise approach for CNN training.Google Scholar
- Gardezi, Syed Jamal Safdar; Awais, Muhammad; Faye, Ibrahima; Meriaudeau, Fabrice. Mammogram classification using Deep learning features. 2017 IEEE International Conference on Signal and Image Processing Applications, Sept. 2017, pp.485--488.Google ScholarCross Ref
- Cheng J, Ni D, Chou Y, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in CT scans. Sci Rep. 2016; 6: 24454.Google Scholar
- Wang J, Yang X, Cai H, et al. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep. 2016; 6: 27327.Google Scholar
Index Terms
- Classification of Benign and Malignant Breast Mass in Digital Mammograms with Convolutional Neural Networks
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