Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis

Abstract

Cancer is a fatal disease caused due to the undesirable spread of cells. Breast carcinoma is the most invasive tumors and is the main reason for cancer deaths in females. Therefore, early diagnosis and prognosis have become necessary to increase survivability and reduce death rates in the long run. New artificial intelligence technologies are assisting radiologists in medical image scrutiny, thereby improving cancer patients’ status. This survey enrolls peer-reviewed, newly developed computer-aided diagnosis (CAD) systems implementing machine learning (ML) and deep learning (DL) techniques for diagnosing breast carcinoma, compares them with previously established methods, and provides technical details with the pros and cons for each model. We also discuss some open issues, research gaps, and future research directions for the advanced CAD models in medical image analysis. Over the past decade, machine learning and deep learning have emerged as a subfield of artificial intelligence (AI), whose healthcare industry applications have provided excellent results with reduced cost and improved efficiency. This survey analyzes different classifiers of machine learning and deep learning approaches for breast cancer diagnosis. Results from previous studies proved that deep learning outperforms conventional machine learning for diagnosing breast carcinoma when the dataset is broad. Research gaps from the recent studies depict that practical and scientific research is an urgent necessity for improving healthcare in the long run.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Abbreviations

AI:

Artificial intelligence

ML :

Machine learning

DL :

Deep learning

CAD:

Computer-aided diagnosis

ANN:

Artificial neural network

CNN:

Convolutional neural network

DCNN:

Deep convolutional neural network

SFM:

Screen film mammography

FFDM :

Full-field digital mammography

DBT:

Digital breast tomosynthesis

MRI:

Magnetic resonance imaging

HP:

Histopathology

US:

Ultrasound

UCI :

University of California Irvine

WBCD:

Wisconsin Breast Cancer Dataset

MIAS:

Mammography Image Analysis Society

DDSM:

Digital Database for Screening Mammography

PCA:

Principal component analysis

SVM:

Support vector machine

KNN:

K-nearest neighbor

LR:

Logistic regression

RBF:

Radial basis function

NB:

Naïve Bayes

DT:

Decision tree

GPU :

Graphical processing unit

LASSO:

Least absolute shrinkage and a selection operator

ROI:

Region of interest

References

  1. 1.

    Goyal K, Prakriti S, Preeti A, Mukesh K. Comparative Analysis of Machine Learning Algorithms for Breast Cancer Prognosis. 2nd International Conference on Communication, Computing and Networking. Springer Singapore; 2019;46:93–102. https://doi.org/10.1007/978-981-13-1217-5.

  2. 2.

    Cancer Statistics-Indian against cancer. Statistics - India Against Cancer [Internet]. 2018 [cited 2020 May 26]. http://cancerindia.org.in/cancer-statistics/.

  3. 3.

    Globocan IAC. Globocan 2018_ India factsheet - India Against Cancer [Internet]. 2018 [cited 2020 May 26]. http://cancerindia.org.in/globocan-2018-india-factsheet/.

  4. 4.

    Saha M, Chakraborty C. Her2Net: a deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation. IEEE Trans Image Process. 2018;27(5):2189–200.

    MathSciNet  Article  Google Scholar 

  5. 5.

    Comparison G. Statistics of breast cancer in india. 2019 [cited 2020 May 26];1–10. https://cytecare.com/blog/statistics-of-breast-cancer/.

  6. 6.

    Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;1(13):8–17. https://doi.org/10.1016/j.csbj.2014.11.005.

  7. 7.

    PBCR. Trends of Breast Cancer in India [Internet]. 2020 [cited 2020 May 26]. https://www.breastcancerindia.net/statistics/trends.html.

  8. 8.

    Khuriwal N, Mishra N. Cancer Diagnosis Using Deep Learning. Proc - IEEE 2018 Int Conf Adv Comput Commun Control Networking, ICACCCN. 2018;98–103.

  9. 9.

    Selvathi D, Poornila AA. Deep Learning Techniques for Breast Cancer Detection Using Medical Image Analysis. Biol Ration Comput Tech Image Process Appl [Internet]. 2018;25:85–109. https://doi.org/10.1007/978-3-319-61316-1.

  10. 10.

    Lai Z, Deng H. Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron. Comput Intell Neurosci. 2018.

  11. 11.

    Duggento A, Scimeca M, Urbano N, Bonanno E, Aiello M, Cavaliere C, et al. A random initialization deep neural network for discriminating malignant breast cancer lesions. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS. 2019;912–5.

  12. 12.

    Huang MW, Chen CW, Lin WC, Ke SW, Tsai CF. SVM and SVM ensembles in breast cancer prediction. PLoS One [Internet]. 2017;12(1):1–14. https://doi.org/10.1371/journal.pone.0161501.

  13. 13.

    Simsek S, Kursuncu U, Kibis E, AnisAbdellatif M, Dag A. A hybrid data mining approach for identifying the temporal effects of variables associated with breast cancer survival. Expert Syst Appl [Internet]. 2020;139:112863. https://doi.org/10.1016/j.eswa.2019.112863.

  14. 14.

    Mihaylov I, Nisheva Ma, Vassilev D. Machine Learning Techniques for Survival Time Prediction in Breast Cancer [Internet]. Springer International Publishing; 2018;1:286–290 .https://doi.org/10.1007/978-3-319-99344-7_17.

  15. 15.

    Kate RJ, Nadig R. Stage-specific predictive models for breast cancer survivability. Int J Med Inform [Internet]. 2017;97:304–11. https://doi.org/10.1016/j.ijmedinf.2016.11.001.

    Article  Google Scholar 

  16. 16.

    Murtaza G, Shuib L, Abdul Wahab AW, Mujtaba G, Mujtaba G, Nweke HF, et al. Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artif Intell Rev [Internet]. 2020;53(3):1655–720. https://doi.org/10.1007/s10462-019-09716-5.

  17. 17.

    Debelee TG, Schwenker F, Ibenthal A, Yohannes D. Survey of deep learning in breast cancer image analysis. Evol Syst [Internet]. 2020;11(1):143–63. https://doi.org/10.1007/s12530-019-09297-2.

    Article  Google Scholar 

  18. 18.

    Zou L, Yu S, Meng T, Zhang Z, Liang X, Xie Y. A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis. Comput Math Methods Med. 2019(Dm).

  19. 19.

    Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep Learning in Medical Image Analysis. Adv Exp Med Biol. 2020;1213:3–21.

    Article  Google Scholar 

  20. 20.

    Yanase J, Triantaphyllou E. The seven key challenges for the future of computer-aided diagnosis in medicine. Int J Med Inform [Internet]. 2019;129:413–22. https://doi.org/10.1016/j.ijmedinf.2019.06.017.

    Article  Google Scholar 

  21. 21.

    Jalalian A, Mashohor SBT, Mahmud HR, Saripan MIB, Ramli ARB, Karasfi B. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: A review. Clin Imaging [Internet]. 2013;37(3):420–6. https://doi.org/10.1016/j.clinimag.2012.09.024..

    Article  Google Scholar 

  22. 22.

    Yassin NIR, Omran S, El Houby EMF, Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review. Comput Methods Prog Biomed [Internet]. 2018;156:25–45. https://doi.org/10.1016/j.cmpb.2017.12.012..

    Article  Google Scholar 

  23. 23.

    Turgut S, Dagtekin M, Ensari T. Microarray breast cancer data classification using machine learning methods. Electr Electron Comput Sci Biomed Eng Meet EBBT. 2018;1–3.

  24. 24.

    Geras KJ, Mann RM, Moy L. Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology. 2019;293(2):246–59.

    Article  Google Scholar 

  25. 25.

    Hu Z, Tang J, Wang Z, Zhang K, Zhang L, Sun Q. Deep learning for image-based cancer detection and diagnosis − A survey. Pattern Recognit [Internet]. 2018;83:134–49. https://doi.org/10.1016/j.patcog.2018.05.014.

    Article  Google Scholar 

  26. 26.

    Carneiro G, Nascimento J, Bradley AP. Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imaging. 2017;36(11):2355–65.

    Article  Google Scholar 

  27. 27.

    Burt JR, Torosdagli N, Khosravan N, Raviprakash H, Mortazi A, Tissavirasingham F, et al. Deep learning beyond cats and dogs: Recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol. 2018;91(1089):20170545.

    Article  Google Scholar 

  28. 28.

    Bevilacqua V, Brunetti A, Guerriero A, Trotta GF, Telegrafo M, Moschetta M. A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images. Cogn Syst Res [Internet]. 2019;53:3–19. https://doi.org/10.1016/j.cogsys.2018.04.011.

    Article  Google Scholar 

  29. 29.

    Sharma S, Mehra R. Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—a Comparative Insight. J Digit Imaging 2020.

  30. 30.

    Worlberg W, Street WN, Mangasarian O. UCI Machine Learning Repository: Breast Cancer Wisconsin (Diagnostic) Data Set [Internet]. http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%2528Diagnostic%2529. 2011 [cited 2020 Dec 11]. https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic).

  31. 31.

    University of Aveiro. Breast Cancer Digital Repository [Internet]. 2018 [cited 2020 Dec 11]. https://bcdr.eu/information/about.

  32. 32.

    Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, et al. Mammographic Image Analysis Society (MIAS) database v1.21. 2015 Aug 28 [cited 2020 Dec 12];  https://www.repository.cam.ac.uk/handle/1810/250394.

  33. 33.

    Suckling J, Boggis CRM, Hutt I, Astley S, Betal D, Cerneaz N, et al. The mini-MIAS database of mammograms [Internet]. The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069. [cited 2020 Dec 12]. 1994;375–8. http://peipa.essex.ac.uk/info/mias.html.

  34. 34.

    Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer P. The Digital Database for Screening Mammography.

  35. 35.

    Spanhol, F., Oliveira, L. S., Petitjean, C. and Heutte L. Breast Cancer Histopathological Database (BreakHis) – Laboratório Visão Robótica e Imagem [Internet]. 2016 [cited 2020 Dec 12]. https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/.

  36. 36.

    Accessing the Data - SEER Datasets [Internet]. [cited 2020 Dec 12]. https://seer.cancer.gov/data/access.html.

  37. 37.

    Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. INbreast: toward a full-field digital mammographic database. Acad Radiol. 2012;19(2):236–48.

    Article  Google Scholar 

  38. 38.

    Oliveira JEE, Gueld MO, de A. Araújo A, Ott B, Deserno TM. Toward a standard reference database for computer-aided mammography. In: Medical Imaging 2008: Computer-Aided Diagnosis [Internet]. 2008;[cited 2020 Dec 13]: 69151Y. http://marathon.csee.usf.edu/mammography/database.html

  39. 39.

    Osareh A, Shadgar B. Machine learning techniques to diagnose breast cancer. 2010 5th Int Symp Heal Informatics Bioinformatics. 2010;114–20.

  40. 40.

    Karabatak M, Ince MC. An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl [Internet]. 2009;36(2):3465–9. https://doi.org/10.1016/j.eswa.2008.02.064.

    Article  Google Scholar 

  41. 41.

    Chen HL, Yang B, Liu J, Liu DY. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl [Internet]. 2011;38(7):9014–22. https://doi.org/10.1016/j.eswa.2011.01.120.

  42. 42.

    Shaikh TA, Rashid A. Applying Machine Learning Algorithms for Early Diagnosis and Prediction of Breast Cancer Risk [Internet]. 2nd International Conference on Communication, Computing and Networking. Springer Singapore; 2019;46:93–102. https://doi.org/10.1007/978-981-13-1217-5.

  43. 43.

    Gupta M, Gupta B. An Ensemble Model for Breast Cancer Prediction Using Sequential Least Squares Programming Method (SLSQP). 2018 11th Int Conf Contemp Comput IC3. 2018;1–3.

  44. 44.

    Kashif M, Malik KR, Jabbar S, Chaudhry J. Application of machine learning and image processing for detection of breast cancer [Internet]. Innovation in Health Informatics. Elsevier Inc.; 2020;145–162. https://doi.org/10.1016/B978-0-12-819043-2.00006-X.

  45. 45.

    Liu F, Brown Mackenzie. Breast Cancer Recognition by Support Vector Machine Combined with Daubechies Wavelet Transform and Principal Component Analysis [Internet]. Vol. 2018. Springer International Publishing; 2018;377–388. https://doi.org/10.1007/978-3-030-00665-5_39.

  46. 46.

    Boughorbel S, Al-Ali R, Elkum N. Model comparison for breast cancer prognosis based on clinical data. PLoS One [Internet]. 2016;11(1):1–15. https://doi.org/10.1371/journal.pone.0146413.

    Article  Google Scholar 

  47. 47.

    Sakai A, Onishi Y, Matsui M, Adachi H, Teramoto A, Saito K, et al. A method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features. Radiol Phys Technol [Internet]. 2020;13(1):27–36. https://doi.org/10.1007/s12194-019-00543-5.

    Article  Google Scholar 

  48. 48.

    Polat K, Güneş S. Breast cancer diagnosis using least square support vector machine. Digit Signal Process A Rev J. 2007;17(4):694–701.

    Article  Google Scholar 

  49. 49.

    Zheng B, Yoon SW, Lam SS. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst Appl [Internet]. 2014;41(4):1476–82. https://doi.org/10.1016/j.eswa.2013.08.044.

    Article  Google Scholar 

  50. 50.

    Asri H, Mousannif H, Al Moatassime H, Noel T. Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis. Procedia Comput Sci [Internet]. 2016;83:1064–9. https://doi.org/10.1016/j.procs.2016.04.224.

    Article  Google Scholar 

  51. 51.

    Ram S, Gupta S. Building Machine Learning Based Diseases Diagnosis System Considering Various Features of Datasets [Internet]. Emerging Trends in Expert Applications and Security. Springer Singapore; 2019;841:357–363. https://doi.org/10.1007/978-981-13-2285-3_17.

  52. 52.

    Al-antari MA, Al-masni MA, Choi MT, Han SM, Kim TS. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform [Internet]. 2018;117:44–54. https://doi.org/10.1016/j.ijmedinf.2018.06.003.

    Article  Google Scholar 

  53. 53.

    Al-masni MA, Al-antari MA, Park JM, Gi G, Kim TY, Rivera P, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed [Internet]. 2018;157:85–94. https://doi.org/10.1016/j.cmpb.2018.01.017.

    Article  Google Scholar 

  54. 54.

    Jung H, Kim B, Lee I, Yoo M, Lee J, Ham S, et al. Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS One. 2018;13(9):1–16.

    Article  Google Scholar 

  55. 55.

    Ting FF, Tan YJ, Sim KS. Convolutional neural network improvement for breast cancer classification. Expert Syst Appl [Internet]. 2019;120:103–15. https://doi.org/10.1016/j.eswa.2018.11.008.

    Article  Google Scholar 

  56. 56.

    Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Guevara Lopez MA. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed. 2016;127:248–57.

    Article  Google Scholar 

  57. 57.

    Jadoon MM, Zhang Q, Haq IU, Butt S, Jadoon A. Three-Class Mammogram Classification Based on Descriptive CNN Features. Biomed Res Int. 2017.

  58. 58.

    Li H, Zhuang S, Li D ao, Zhao J, Ma Y. Benign and malignant classification of mammogram images based on deep learning. Biomed Signal Process Control [Internet]. 2019;51:347–54. https://doi.org/10.1016/j.bspc.2019.02.017

  59. 59.

    Li S, Wei J, Chan HP, Helvie MA, Roubidoux MA, Lu Y, et al. Computer-aided assessment of breast density: Comparison of supervised deep learning and feature based statistical learning. Phys Med Biol. 2018.

  60. 60.

    Mughal B, Muhammad N, Sharif M. Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain. Int J Med Inform [Internet]. 2019;126:26–34. https://doi.org/10.1016/j.ijmedinf.2019.02.001.

  61. 61.

    Gao F, Wu T, Li J, Zheng B, Ruan L, Shang D, et al. SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis. Comput Med Imaging Graph [Internet]. 2018;70:53–62. https://doi.org/10.1016/j.compmedimag.2018.09.004.

    Article  Google Scholar 

  62. 62.

    Cai H, Huang Q, Rong W, Song Y, Li J, Wang J, et al. Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms. Comput Math Methods Med. 2019.

  63. 63.

    Sudharshan PJ, Petitjean C, Spanhol F, Oliveira LE, Heutte L, Honeine P. Multiple instance learning for histopathological breast cancer image classification. Expert Syst Appl [Internet]. 2019;117:103–11. https://doi.org/10.1016/j.eswa.2018.09.049.

    Article  Google Scholar 

  64. 64.

    Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Cha K, Richter C. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys Med Biol. 2017;8894–8908.

  65. 65.

    Yousefi M, Krzyżak A, Suen CY. Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. Comput Biol Med. 2018;96(February):283–93.

    Article  Google Scholar 

  66. 66.

    Yap MH, Pons G, Martí J, Ganau S, Sentís M, Zwiggelaar R, et al. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Heal Informatics. 2018;22(4):1218–26.

    Article  Google Scholar 

  67. 67.

    Qi X, Zhang L, Chen Y, Pi Y, Chen Y, Lv Q, et al. Automated diagnosis of breast ultrasonography images using deep neural networks. Med Image Anal [Internet]. 2019;52:185–98. https://doi.org/10.1016/j.media.2018.12.006.

    Article  Google Scholar 

  68. 68.

    Araujo T, Aresta G, Castro E, Rouco J, Agular P, Eloy C, et al. Classification of breast cancer based on histology images using convolutional neural networks. PLoS One. 2017;6:24680–93.

    Google Scholar 

  69. 69.

    Roy K, Banik D, Bhattacharjee D, Nasipuri M. Patch-based system for Classification of Breast Histology images using deep learning. Comput Med Imaging Graph [Internet]. 2019;71:90–103. https://doi.org/10.1016/j.compmedimag.2018.11.003.

    Article  Google Scholar 

  70. 70.

    Li Y, Wu J, Wu Q. Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access. 2019;7:21400–8.

    Article  Google Scholar 

  71. 71.

    Deniz E, Şengür A, Kadiroğlu Z, Guo Y, Bajaj V, Budak Ü. Transfer learning based histopathologic image classification for breast cancer detection. Heal Inf Sci Syst [Internet]. 2018;6(1). https://doi.org/10.1007/s13755-018-0057-x.

  72. 72.

    Brancati N, De Pietro G, Frucci M, Riccio D. A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images. IEEE Access. 2019;7(c):44709–20.

  73. 73.

    Jiang Y, Chen L, Zhang H, Xiao X. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS One. 2019;14(3):1–21.

    Google Scholar 

  74. 74.

    Nahid A Al, Mehrabi MA, Kong Y. Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. Biomed Res Int. 2018.

  75. 75.

    Yan R, Ren F, Rao X, Shi B, Xiang T, Zhang L, et al. Integration of Multimodal Data for Breast Cancer Classification Using a Hybrid Deep Learning Method [Internet]. LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing; 2019;11643:460–469.  https://doi.org/10.1007/978-3-030-26763-6_44.

  76. 76.

    Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, et al. Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans Nanobioscience. 2018;17(3):237–42.

    Article  Google Scholar 

  77. 77.

    Samala RK, Chan HP, Hadjiiski L, Helvie MA, Wei J, Cha K. Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med Phys. 2016;43(12):6654–66.

    Article  Google Scholar 

  78. 78.

    Fan M, Li Y, Zheng S, Peng W, Tang W, Li L. Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network. Methods [Internet]. 2019;166:103–11. https://doi.org/10.1016/j.ymeth.2019.02.010.

    Article  Google Scholar 

  79. 79.

    Zhang D, Zou L, Zhou X, He F. Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE Access. 2018;6(8):28936–44.

    Article  Google Scholar 

  80. 80.

    Zhang X, Zhang Y, Zhang Q, Ren Y, Qiu T, Ma J, et al. Extracting comprehensive clinical information for breast cancer using deep learning methods. Int J Med Inform [Internet]. 2019;132:103985. https://doi.org/10.1016/j.ijmedinf.2019.103985.

    Article  Google Scholar 

  81. 81.

    Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Richter CD. Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis. Phys Med Biol. 2020;in press.

  82. 82.

    Sun W, Tseng TLB, Zhang J, Qian W. Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Graph [Internet]. 2017;57:4–9. https://doi.org/10.1016/j.compmedimag.2016.07.004.

    Article  Google Scholar 

  83. 83.

    Yurttakal AH, Erbay H, İkizceli T, Karaçavuş S. Detection of breast cancer via deep convolution neural networks using MRI images. Multimed Tools Appl. 2019.

  84. 84.

    Vo DM, Nguyen NQ, Lee SW. Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci (Ny) [Internet]. 2019;482:123–38. https://doi.org/10.1016/j.ins.2018.12.089.

  85. 85.

    Li L, Pan X, Yang H, Liu Z, He Y, Li Z, et al. Multi-task Deep Learning for Fine-Grained Classification and grading in Breast Cancer Histopathological Images. Multimed Tools Appl. 2018.

  86. 86.

    Samala RK, Chan HP, Hadjiiski L, Helvie MA, Richter CD, Cha KH. Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imaging. 2019;38(3):686–96.

    Article  Google Scholar 

  87. 87.

    Li X, Qin G, He Q, Sun L, Zeng H, He Z, et al. Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification. Eur Radiol. 2020;30(2):778–88.

    Article  Google Scholar 

  88. 88.

    Chougrad H, Zouaki H, Alheyane O. Deep Convolutional Neural Networks for breast cancer screening. Comput Methods Programs Biomed [Internet]. 2018;157:19–30. https://doi.org/10.1016/j.cmpb.2018.01.011.

    Article  Google Scholar 

  89. 89.

    Antropova N, Huynh BQ, Giger ML. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys. 2017;44:5162–71.

    Article  Google Scholar 

  90. 90.

    Samala RK, Chan HP, Hadjiiski LM, Helvie MA, Richter C, Cha K. Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis. Phys Med Biol. 2018;63.

  91. 91.

    Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2017;19(6):1236–46.

    Article  Google Scholar 

  92. 92.

    Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal [Internet]. 2018;47:45–67. https://doi.org/10.1016/j.media.2018.03.006.

    Article  Google Scholar 

  93. 93.

    Arefan D, Mohamed AA, Berg WA, Zuley ML, Sumkin JH, Wu S. Deep learning modeling using normal mammograms for predicting breast cancer risk. Med Phys. 2020;47(1):110–8.

    Article  Google Scholar 

  94. 94.

    Kleinlein R, Riaño D. Persistence of data-driven knowledge to predict breast cancer survival. Int J Med Inform [Internet]. 2019;129(June):303–11. https://doi.org/10.1016/j.ijmedinf.2019.06.018.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Shailender Kumar.

Ethics declarations

Ethics approval and informed consent

The article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

This is not required as no human participants are involved.

Conflict of Interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chugh, G., Kumar, S. & Singh, N. Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis. Cogn Comput (2021). https://doi.org/10.1007/s12559-020-09813-6

Download citation

Keywords

  • Breast cancer
  • Machine learning
  • Deep learning
  • Convolution neural network (CNN)
  • Computer-aided diagnosis (CAD)