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Cognitive Computation

Erschienen in:

30.01.2021

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

verfasst von: Gunjan Chugh, Shailender Kumar, Nanhay Singh

Erschienen in: Cognitive Computation | Ausgabe 6/2021

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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.

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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.

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

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

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.MathSciNetCrossRef

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

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.

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

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

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.

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.

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.

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.

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.

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.

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.CrossRef

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.

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.CrossRef

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.

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

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.CrossRef

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..CrossRef

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..CrossRef

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.

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

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.

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.

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

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.

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

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.CrossRef

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.

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

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.CrossRef

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.

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.

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.

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.

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.

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.

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.

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.CrossRef

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.

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.CrossRef

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.CrossRef

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.CrossRef

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.

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.

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.

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.

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.CrossRef

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.

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.

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

Titel: Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis
verfasst von: Gunjan Chugh
Shailender Kumar
Nanhay Singh
Publikationsdatum: 30.01.2021
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 6/2021
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-020-09813-6

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