research-article

Classification of Benign and Malignant Breast Mass in Digital Mammograms with Convolutional Neural Networks

Authors:

Hongkai WangAuthors Info & Claims

ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine

Pages 47 - 50

https://doi.org/10.1145/3285996.3286006

Published: 13 October 2018 Publication History

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.

References

[1]

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.

[2]

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.

[3]

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.

[4]

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).

Digital Library

[5]

Akay, M. F. Support vector machines combined with feature selection for breast cancer diagnosis. Expert systems with applications 36(2), 3240--3247 (2009).

Digital Library

[6]

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).

Digital Library

[7]

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).

[8]

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.

[9]

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.

Digital Library

[10]

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.

[11]

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.

[12]

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.

[13]

LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, pp. 436--444, 2015.

[14]

M. I. Razzak, S. Naz, and A. Zaib, "Deep Learning for Medical Image Processing: Overview, Challenges and Future," arXiv preprint arXiv:1704.06825, 2017.

[15]

D. Shen, G. Wu, and H.-I. Suk, "Deep learning in medical image analysis," Annual Review of Biomedical Engineering, 2017.

[16]

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.

[17]

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.

[18]

Wang D, Khosla A, Gargeya R, et al. Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718, 2016.

[19]

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.

[20]

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 pages

[21]

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--321

[22]

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.

[23]

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.

[24]

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.

[25]

Wang J, Yang X, Cai H, et al. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep. 2016; 6: 27327.

Cited By

Zaiter LZwiggelaar R(2024)Segmentation and classification of mammographic abnormalities using local binary patterns and deep learning17th International Workshop on Breast Imaging (IWBI 2024)10.1117/12.3026886(55)Online publication date: 29-May-2024
https://doi.org/10.1117/12.3026886
Laishram RRabidas R(2024)An optimized ensemble classifier for mammographic mass classificationComputers and Electrical Engineering10.1016/j.compeleceng.2024.109488119(109488)Online publication date: Oct-2024
https://doi.org/10.1016/j.compeleceng.2024.109488
Guanulema BOsorio-Ordóñez DVaca-Farinango HMieles-Salazar AVillalba-Meneses FCevallos-Bermeo GCadena-Morejon CAlmeida-Galárraga DTirado-Espin A(2023)Early Detection of Breast Cancer Using Pretrained AlexNet Convolutional Neural Network2023 Fourth International Conference on Information Systems and Software Technologies (ICI2ST)10.1109/ICI2ST62251.2023.00019(81-88)Online publication date: 22-Nov-2023
https://doi.org/10.1109/ICI2ST62251.2023.00019
Show More Cited By

Index Terms

Classification of Benign and Malignant Breast Mass in Digital Mammograms with Convolutional Neural Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine

October 2018

166 pages

ISBN:9781450365338

DOI:10.1145/3285996

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Publication History

Published: 13 October 2018

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ISICDM 2018: The 2nd International Symposium on Image Computing and Digital Medicine 2018

October 13 - 14, 2018

Chengdu, China

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Cited By

Zaiter LZwiggelaar R(2024)Segmentation and classification of mammographic abnormalities using local binary patterns and deep learning17th International Workshop on Breast Imaging (IWBI 2024)10.1117/12.3026886(55)Online publication date: 29-May-2024
https://doi.org/10.1117/12.3026886
Laishram RRabidas R(2024)An optimized ensemble classifier for mammographic mass classificationComputers and Electrical Engineering10.1016/j.compeleceng.2024.109488119(109488)Online publication date: Oct-2024
https://doi.org/10.1016/j.compeleceng.2024.109488
Guanulema BOsorio-Ordóñez DVaca-Farinango HMieles-Salazar AVillalba-Meneses FCevallos-Bermeo GCadena-Morejon CAlmeida-Galárraga DTirado-Espin A(2023)Early Detection of Breast Cancer Using Pretrained AlexNet Convolutional Neural Network2023 Fourth International Conference on Information Systems and Software Technologies (ICI2ST)10.1109/ICI2ST62251.2023.00019(81-88)Online publication date: 22-Nov-2023
https://doi.org/10.1109/ICI2ST62251.2023.00019
Duong LChu CNguyen PNguyen STran B(2023)Edge detection and graph neural networks to classify mammograms: A case study with a dataset from Vietnamese patientsApplied Soft Computing10.1016/j.asoc.2022.109974134(109974)Online publication date: Feb-2023
https://doi.org/10.1016/j.asoc.2022.109974
Oza PSharma PPatel SKumar P(2022)Deep convolutional neural networks for computer-aided breast cancer diagnostic: a surveyNeural Computing and Applications10.1007/s00521-021-06804-yOnline publication date: 11-Jan-2022
https://doi.org/10.1007/s00521-021-06804-y
Oza PSharma PPatel S(2022)A Drive Through Computer-Aided Diagnosis of Breast Cancer: A Comprehensive Study of Clinical and Technical AspectsRecent Innovations in Computing10.1007/978-981-16-8248-3_19(233-249)Online publication date: 10-Mar-2022
https://doi.org/10.1007/978-981-16-8248-3_19
Oyetade IAyeni JOgunde AOguntunde BOlowookere T(2021)Hybridized Deep Convolutional Neural Network and Fuzzy Support Vector Machines for Breast Cancer DetectionSN Computer Science10.1007/s42979-021-00882-43:1Online publication date: 10-Nov-2021
https://doi.org/10.1007/s42979-021-00882-4
Buettner RBilo MBay NZubac T(2020)A Systematic Literature Review of Medical Image Analysis Using Deep Learning2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA)10.1109/ISIEA49364.2020.9188131(1-4)Online publication date: Jul-2020
https://doi.org/10.1109/ISIEA49364.2020.9188131

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