Deep Learning-based Mammogram Classification for Breast Cancer

Keywords: Deep Learning, Convolutional Neural Networks, Breast Cancer, Mammogram, DDSM, Transfer Learning


Deep Learning (DL) is a rising field of researches in the last decade by exposing a hybrid analysis procedure including advanced level image processing and many efficient supervised classifiers. Robustness of the DL algorithms to the big data enhances the analysis capabilities of machine learning models by feature learning on heterogeneous image database. In this paper, Convolutional Neural Network (CNN) architecture was proposed on simplified feature learning and fine-tuned classifier model to separate cancer-normal cases on mammograms. Breast Cancer is a prevalent and mortal disease that appeared resultant mutating of normal tissue into tumor pathology. Mammograms are common and effective tools for the diagnosis of breast cancer. DL-based computer-assisted systems have the capability of detailed analysis for even small pathology that may lead the curing progress for a complete assessment. The proposed DL-based model aimed at assessing the applicability of various feature-learning models and enhancing the learning capacity of the DL models for an operative breast cancer diagnosis using CNN. The mammograms were fed into the DL to evaluate the classification performances in accordance with various CNN architectures. The proposed Deep model achieved high classification performance rates of 92.84%, 95.30%, and 96.72% for accuracy, sensitivity, specificity, and precision, respectively.


Download data is not yet available.


E. A. Sickles, “Screening for breast cancer with mammography,” Clinical Imaging. 1991, doi: 10.1016/s0140-6736(01)07198-7.

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,” Clin. Imaging, vol. 37, no. 3, pp. 420–426, May 2013, doi: 10.1016/j.clinimag.2012.09.024.

Q. Zeng, H. Jiang, and L. Ma, “Learning multi-level features for breast mass detection,” in ACM International Conference Proceeding Series, 2018, doi: 10.1145/3285996.3286000.

H. P. Chan, R. K. Samala, L. M. Hadjiiski, and C. Zhou, “Deep Learning in Medical Image Analysis,” in Advances in Experimental Medicine and Biology, 2020.

J. Shiraishi, Q. Li, D. Appelbaum, and K. Doi, “Computer-aided diagnosis and artificial intelligence in clinical imaging,” Seminars in Nuclear Medicine. 2011, doi: 10.1053/j.semnuclmed.2011.06.004.

M. A. Al-antari et al., “An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network,” J. Med. Biol. Eng., 2018, doi: 10.1007/s40846-017-0321-6.

S. J. A. Sarosa, F. Utaminingrum, and F. A. Bachtiar, “Mammogram Breast Cancer Classification Using Gray-Level Co-Occurrence Matrix and Support Vector Machine,” in 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018 - Proceedings, 2018, doi: 10.1109/SIET.2018.8693146.

R. Wang et al., “Multi-level nested pyramid network for mass segmentation in mammograms,” Neurocomputing, vol. 363, pp. 313–320, Oct. 2019, doi: 10.1016/j.neucom.2019.06.045.

W. Zhu, X. Xiang, T. D. Tran, G. D. Hager, and X. Xie, “Adversarial deep structured nets for mass segmentation from mammograms,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 847–850, doi: 10.1109/ISBI.2018.8363704.

G. Altan and Y. Kutlu, “Hessenberg Elm Autoencoder Kernel For Deep Learning,” J. Eng. Technol. Appl. Sci., 2018, doi: 10.30931/jetas.450252.

G. Altan, Y. Kutlu, A. Ö. Pekmezci, and A. Yayık, “Diagnosis of Chronic Obstructive Pulmonary Disease using Deep Extreme Learning Machines with LU Autoencoder Kernel,” in 7th International Conference on Advanced Technologies (ICAT’18), 2018.

D. Abdelhafiz, C. Yang, R. Ammar, and S. Nabavi, “Deep convolutional neural networks for mammography: Advances, challenges and applications,” BMC Bioinformatics, 2019, doi: 10.1186/s12859-019-2823-4.

K. Mendel, H. Li, D. Sheth, and M. Giger, “Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography,” Acad. Radiol., vol. 26, no. 6, pp. 735–743, Jun. 2019, doi: 10.1016/j.acra.2018.06.019.

J. Arevalo, A. Cruz-Roa, and F. A. González, “Hybrid image representation learning model with invariant features for basal cell carcinoma detection,” 2013, pp. 89220M--6, doi: 10.1117/12.2035530.

J. Arevalo et al., “DeepMammo Breast Mass Classification using Deep Convolutional Neural Networks,” Comput. Methods Programs Biomed., 2018, doi: 10.1016/j.acra.2018.06.019.

S. Pan, J. Zhang, T. Wang, and L. Kong, “X-Ray Mammary Image Segmentation Based on Convolutional Neural Network,” in 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), 2019, pp. 105–108, doi: 10.1109/ICIVC47709.2019.8981350.

S. Yoon and S. Kim, “AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM,” BMC Med. Inform. Decis. Mak., 2009, doi: 10.1186/1472-6947-9-S1-S1.

P. Xi, C. Shu, and R. Goubran, “Abnormality Detection in Mammography using Deep Convolutional Neural Networks,” in 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2018, pp. 1–6, doi: 10.1109/MeMeA.2018.8438639.

B. Pardamean, T. W. Cenggoro, R. Rahutomo, A. Budiarto, and E. K. Karuppiah, “Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram Data,” Procedia Comput. Sci., vol. 135, pp. 400–407, 2018, doi: 10.1016/j.procs.2018.08.190.

M. G. Ertosun and D. L. Rubin, “Probabilistic visual search for masses within mammography images using deep learning,” in 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015, pp. 1310–1315, doi: 10.1109/BIBM.2015.7359868.

S. Suzuki et al., “Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis,” in 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 2016, pp. 1382–1386, doi: 10.1109/SICE.2016.7749265.

V. D. Nguyen, K. Lim, M. D. Le, and N. Dung Bui, “Combination of Gabor Filter and Convolutional Neural Network for Suspicious Mass Classification,” in 2018 22nd International Computer Science and Engineering Conference (ICSEC), 2018, pp. 1–4, doi: 10.1109/ICSEC.2018.8712796.

R. Touahri, N. AzizI, N. E. Hammami, M. Aldwairi, and F. Benaida, “Automated Breast Tumor Diagnosis Using Local Binary Patterns (LBP) Based on Deep Learning Classification,” in 2019 International Conference on Computer and Information Sciences (ICCIS), 2019, pp. 1–5, doi: 10.1109/ICCISci.2019.8716428.

R. M. and P. K. J. M. Heath, K. Bowyer, D. Kopans, “The Digital Database for Screening Mammography,” in the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed., Medical Physics Publishing, 2001., 2001, doi: ISBN 1-930524-00-5.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst., 2012, doi:

D. C. Cireşan, U. Meier, L. M. Gambardella, and J. Schmidhuber, “Convolutional neural network committees for handwritten character classification,” in Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2011, doi: 10.1109/ICDAR.2011.229.

G. Altan, “DeepGraphNet: Grafiklerin Sınıflandırılmasında Derin Öğrenme Modelleri,” Eur. J. Sci. Technol., pp. 319–327, Oct. 2019, doi: 10.31590/ejosat.638256.

G. Altan and Y. Kutlu, “Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis,” Nat. Eng. Sci., 2018, doi: 10.28978/nesciences.468978.

G. Altan, Y. Kutlu, and N. Allahverdi, “Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 5, pp. 1344–1350, May 2020, doi: 10.1109/JBHI.2019.2931395.

H. Nasir Khan, A. R. Shahid, B. Raza, A. H. Dar, and H. Alquhayz, “Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network,” IEEE Access, 2019, doi: 10.1109/ACCESS.2019.2953318.

B. Swiderski, J. Kurek, S. Osowski, M. Kruk, and W. Barhoumi, “Deep learning and non-negative matrix factorization in recognition of mammograms,” in Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 2017, doi: 10.1117/12.2266335.

How to Cite
G. Altan, “Deep Learning-based Mammogram Classification for Breast Cancer”, IJISAE, vol. 8, no. 4, pp. 171-176, Dec. 2020.
Research Article