Skip to main content

Advertisement

Springer Nature Link
Log in

Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Advances in deep learning networks, especially deep convolutional neural networks (DCNNs), are causing remarkable breakthroughs in radiology and imaging sciences. These advances have influenced the development of computer-aided diagnosis (CAD). This study presents applications of DCNNs for computer-aided breast cancer diagnosis. We discuss the recent breakthrough, achievements, and notable advances in CAD for breast cancer. Various key and novel insights and challenges on the use of DCNNs for mammogram analysis have been presented in the paper. The latest deep learning toolkits and libraries that are available and insights for using them have been elaborated. We also point out the possible limitations in the use of DCNNs for breast cancer detection. Finally, give some ideas of future research which can address the existing limitations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bray F (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424

    Article  Google Scholar 

  2. Cancer Facts & Figures (2020), [Online]. Available: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/ cancer-facts-figures-2020.html. Accessed on: (March 28, 2021)

  3. Alessandro B et al (2020) A novel solution based on scale invariant feature transform descriptors and deep learning for the detection of suspicious regions in mammogram images. J Med Sig Sens 10(3):158

    Google Scholar 

  4. Debelee TG et al (2020) Survey of deep learning in breast cancer image analysis. Evolv Syst 11(1):143–163

    Google Scholar 

  5. Al-antari Mugahed A et al (2018) An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. J Med Biol Eng 38(3):443–456

    Google Scholar 

  6. Al-Masni MA et al (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Meth Prog Biomed 157:85–94

    Google Scholar 

  7. Al-Antari Mugahed A et al (2018) A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform 117:44–54

    Google Scholar 

  8. Zou L et al (2019) A technical review of convolutional neural network-based mammographic breast cancer diagnosis. Comput Math Meth Med

  9. Burt Jeremy R et al (2018) Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. British J Radiol 91(1089):20170545

    Google Scholar 

  10. Anna C-G et al (2016) Positive psychological functioning in breast cancer: an integrative review. Breast 27:136–168

    Google Scholar 

  11. Muramatsu C et al (2016) Breast mass classification on mammograms using radial local ternary patterns. Comp Biol Med 72:43–53

    Google Scholar 

  12. Jitendra V et al (2016) PCA-PNN and PCA-SVM-based CAD systems for breast density classification. Applications of intelligent optimization in biology and medicine. Springer, Cham, pp 159–180

    Google Scholar 

  13. Yassin NIR et al (2018) Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comp Meth Prog Biomed 156:25–45

    Google Scholar 

  14. Haixia L et al (2017) Breast masses in mammography classification with local contour features. Biomed Eng online 16(1):44–55

    Google Scholar 

  15. Neeraj D et al (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 37:114–128

    Google Scholar 

  16. Jamal GS, Safdar, et al (2019) Breast cancer detection and diagnosis using mammographic data: systematic review. J Med Int Res 21(7):e14464

  17. Gustavo C et al (2017) Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imag 36(11):2355–2365

    Google Scholar 

  18. Peyman R et al (2012) Mammography segmentation with maximum likelihood active contours. Med Image Anal 16(6):1167–1186

    Google Scholar 

  19. Ying W et al (2011) Mammographic mass segmentation: embedding multiple features in vector-valued level set in ambiguous regions. Patt Recog 44(9):1903–1915

    Google Scholar 

  20. Rojas DA, Nandi Asoke K (2009) Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. Patt Recog 42(6):1138–1148

    Google Scholar 

  21. Thijs K et al (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303–312

    Google Scholar 

  22. Dhungel N et al (2015) Deep structured learning for mass segmentation from mammograms, IEEE International Conference on Image Processing (ICIP). QC, Canada, Quebec City, pp 2950–2954

  23. Zhicheng J et al (2016) A deep feature-based framework for breast masses classification. Neurocomputing 197:221–231

    Google Scholar 

  24. (2001) The digital database for screening mammography [Online]. Available:http://www.eng.usf.edu/cvprg/Mammography/Database.html

  25. Suckling J et al (1994) The mammographic image analysis society digital mammogram database exerpta medica. Int Congr Ser 1069:375–378

    Google Scholar 

  26. Moreira IC et al (2012) INbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236–248

    Google Scholar 

  27. Lopez MG, et al (2012) BCDR: a breast cancer digital repository 15th International Conference on Experimental Mechanics

  28. Oliveira Júlia EE et al (2008) Toward a standard reference database for computer-aided mammography. Med Imag 2008: Comput Aid Diag 6915

  29. Rikiya Y et al (2018) Convolutional neural networks: an overview and application in radiology. Insights Imag 9(4):611–629

    Google Scholar 

  30. Dina A et al (2019) Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinform 20(11):75–94

    Google Scholar 

  31. Pedamonti D (2018) Comparison of non-linear activation functions for deep neural networks on MNIST classification task. arXiv preprint arXiv:1804.02763

  32. Glorot X et al (2011) Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics : 315-323

  33. Maas Andrew L et al (2013) Rectifier nonlinearities improve neural network acoustic models. Int Conf Mach Learn Atlanta Georg USA 30:1

    Google Scholar 

  34. Adnan Q et al (2017) Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8–20

    Google Scholar 

  35. Mohamed Aly A et al (2018) Understanding clinical mammographic breast density assessment: a deep learning perspective. J Dig Imag 31(4):387–392

    Google Scholar 

  36. Wichakam I, Vateekul P (2016) Combining deep convolutional networks and SVMs for mass detection on digital mammograms. presented in 8th IEEE International Conference on Knowledge and Smart Technology 239-44

  37. Vasilev I et al (2019) Neural networks, in python deep learning: exploring deep learning techniques and neural network architectures with Pytorch, Keras. and TensorFlow, 2nd edn. Packt Publishing Ltd, BIRMINGHAM - MUMBAI, India, pp 43–44

  38. Alex K, Ilya S, Hinton Geoffrey E (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inform Proc Syst 25:1097–1105

    Google Scholar 

  39. Yann LC et al (1998) Gradient-based learning applied to document recognition. Proceed IEEE 86(11):2278–2324

    Google Scholar 

  40. Asifullah K et al (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516

    Google Scholar 

  41. Simonyan K, Andrew Z (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556v6 [cs.CV]. 1-14

  42. Szegedy Christian et al (2015) Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition: 1-12

  43. He K, Sun J (2015) Convolutional neural networks at constrained time cost. Proceedings of the IEEE conference on computer vision and pattern recognition. 5353–5360

  44. Srivastava RK et al (2015) Highway networks. arXiv preprint<error l="295" c="End of data reached while scanning argument" /><hyperimage arxiv="http://arxiv.org/abs/arXiv:1505.00387v2" />

  45. He K et al (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition: 770-778

  46. Szegedy, Christian, et al (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition : 2818-2826

  47. Szegedy C et al (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) pp 4278–4284

  48. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition pp 1800–1807

  49. Redmon, Joseph, et al (2016). You only look once: unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition: 779-788

  50. Long, Jonathan, et al(2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition : 3431-3440

  51. Ronneberger, Olaf, et al (2015). U-net: convolutional networks for biomedical image segmentation. Proceeding of International Conference on Medical image computing and computer-assisted intervention : 234-241

  52. He, Kaiming, et al (2017). Mask r-cnn. Proceedings of the IEEE international conference on computer vision : 2980-2988

  53. Ren S et al (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Patt Anal Mach Intell 39(6):1137–1149

    Google Scholar 

  54. Wenqing S et al (2017) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imag Graph 57:4–9

    Google Scholar 

  55. Dezsø R et al (2018) Detecting and classifying lesions in mammograms with deep learning. Scient Rep 8(1):1–7

    MathSciNet  Google Scholar 

  56. Diniz JO, Bandeira, et al (2016) Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks. Comput Meth Prog Biomed 156:191–207

  57. Yousefi M et al (2018) Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning. Comput Biol Med 96:283–293

    Google Scholar 

  58. SanaUllah K et al (2019) A novel deep learning-based framework for the detection and classification of breast cancer using transfer learning. Patt Recog Lett 125:1–6

    Google Scholar 

  59. Rongbo S et al (2019) Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning. Fut Gen Comput Syst 101:668–679

    Google Scholar 

  60. Benedetta S et al (2020) A multi-context CNN ensemble for small lesion detection. Artif Intell Med 103:101749

  61. Agarwal R et al (2020) Deep learning for mass detection in full field digital mammograms. Comput Biol Med

  62. Al-Antari et al (2020) Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput Meth Prog Biomed. 196:105584

  63. Yanfeng L et al (2020) Mass detection in mammograms by bilateral analysis using convolution neural network. Comput Meth Prog Biomed. 195:105518

  64. Rongbo S et al (2020) Unsupervised domain adaptation with adversarial learning for mass detection in mammogram. Neurocomputing 393:27–37

    Google Scholar 

  65. Aly GH et al (2020) YOLO-based breast masses detection and classification in full-field digital mammograms. Comput Meth Prog Biomed. 200:105823

  66. Xi P, Shu C, Goubran R (2018) Abnormality detection in mammography using deep convolutional neural networks, IEEE International Symposium on Medical Measurements and Applications (MeMeA). Italy, Rome, pp 1–6

  67. Deep DS et al (2020) Breast cancer detection and classification using global pooling, 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp 1–5

  68. Abdel Rahman AS et al (2020) Breast mass tumor classification using deep learning, IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar, : 271-276

  69. Xiaofei Z et al (2018) Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks. IEEE Trans Nanobiosci 17(3):237–242

    Google Scholar 

  70. Djebbar K et al (2019) Deep convolutional neural networks for detection and classification of tumors in mammograms, 6th International Conference on Image and Signal Processing and their Applications (ISPA). Algeria, Mostaganem, pp 1–7

  71. Gnanasekaran VS et al (2020) Deep learning algorithm for breast masses classification in mammograms. IET Image Proc. 14(12):2860–2868

  72. Xin S et al (2020) Deep neural networks with region-based pooling structures for mammographic image classification. IEEE Trans Med Imag 39(6):2246–2255

    Google Scholar 

  73. Al-Masni et al. (2017). Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) :1230-1233

  74. Gardezi SJ, Safdar, et al (2017) Mammogram classification using deep learning features IEEE International Conference on Signal and Image Processing Applications (ICSIPA) pp 485–488

  75. Lazaros T et al (2019) Deep learning for breast cancer diagnosis from mammograms-a comparative study. J Imag 5(3):37

    Google Scholar 

  76. Carneiro G et al (2017) Deep learning models for classifying mammogram exams containing unregistered multi-view images and segmentation maps of lesions. in Deep learning for medical image analysis Academic Press. pp 321–339

  77. Zhu W et al (2018) Adversarial deep structured nets for mass segmentation from mammograms. IEEE 15th International Symposium on Biomedical Imaging (ISBI) : 847–850

  78. Andrik R et al (2019) Breast pectoral muscle segmentation in mammograms using a modified holistically nested edge detection network. Med Image Anal 57:1–17

    Google Scholar 

  79. Shen T et al (2019) Learning from adversarial medical images for X-ray breast mass segmentation. Comput Meth Prog Biomed. 180:105012

  80. Hossain MDS (2019) Micro calcification segmentation using modified U-net segmentation network from mammogram images. J King Saud Univ Comput Inform Sci

  81. Runze W et al (2019) Multi-level nested pyramid network for mass segmentation in mammograms. Neurocomputing 363:313–320

    Google Scholar 

  82. Shuyi L et al (2019) Attention dense-u-net for automatic breast mass segmentation in digital mammogram. IEEE Access 7:59037–59047

    Google Scholar 

  83. Pérez-Benito FJ et al (2020) A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Comput Meth Prog Biomed. 195:105668

  84. Ghosh Swarup KR et al (2021) A novel intuitionistic fuzzy soft set entrenched mammogram segmentation under Multigranulation approximation for breast cancer detection in early stages. Exp Syst Appl. 169:114329

  85. Singh VK et al (2020) Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Exp Syst Appl. 139:112855

  86. Qian Yu et al (2021) Crossover-Net: leveraging vertical-horizontal crossover relation for robust medical image segmentation. Pattern Recog. 113:107756

  87. Juan C et al (2020) A novel multi-scale adversarial networks for precise segmentation of X-ray breast mass. IEEE Access 8:103772–103781

    Google Scholar 

  88. Dina A et al (2020) Convolutional neural network for automated mass segmentation in mammography. BMC Bioinf 21(1):1–19

    Google Scholar 

  89. Yu Hui et al (2020). Deep learning-based fully automated detection and segmentation of breast mass. 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). Chengdu, China, pp 293–298

  90. Soleimani H, Michailovich OV (2020) On segmentation of pectoral muscle in digital mammograms by means of deep learning. IEEE Access 8:204173–204182

    Google Scholar 

  91. Tianyu S et al (2019) Simultaneous segmentation and classification of mass region from mammograms using a mixed-supervision guided deep model. IEEE Sig Proc Lett 27:196–200

    Google Scholar 

  92. Mehreen T et al (2020) Medical image-based breast cancer diagnosis: state of the art and future directions. Exp Syst Appl. 167:114095

  93. Huynh Benjamin Q et al (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imag 3(3):034501

  94. Jiang F et al (2017) Breast mass lesion classification in mammograms by transfer learning. Proceedings of the 5th International Conference on Bioinformatics and Computational Biology. ACM. pp 59–62

  95. Chougrad, Hiba, et al (2017). Convolutional neural networks for breast cancer screening: transfer learning with exponential decay. arXiv preprint arXiv:1711.10752v1 [cs.CV]

  96. Samala Ravi K et al (2017) Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys Med Biol 62(23):8894

    Google Scholar 

  97. Hiba C et al (2020) Multi-label transfer learning for the early diagnosis of breast cancer. Neurocomputing 392:168–180

    Google Scholar 

  98. Yemini M et al (2018) Detecting masses in mammograms using convolutional neural networks and transfer learning, IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE). Israel, Eilat, pp 1–4

  99. Falconí LG et al (2019). Transfer learning in breast mammogram abnormalities classification With mobilenet and nasnet, International Conference on Systems, Signals and Image Processing (IWSSIP), Osijek, Croatia, pp 109–114

  100. Kamrul H et al (2020) Automatic mass classification in breast using transfer learning of deep convolutional neural network and support vector machine. IEEE Region 10 Symposium (TENSYMP) p 110:113

  101. Mednikov Y et al (2018) Transfer representation learning using inception-V3 for the detection of masses in Mammography, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). HI, USA, Honolulu, pp 2587–2590

  102. Guan S, Loew M (2017) Breast cancer detection using transfer learning in convolutional neural networks, IEEE Applied Imagery Pattern Recognition Workshop (AIPR). USA, Washington, DC, pp 1–8

  103. Falconí L et al (2020) Transfer learning and fine tuning in Mammogram BI-RADS classification. IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) p 475:480

  104. Kukačka J et al. (2017) Regularization for deep learning: A taxonomy. arXiv preprintarXiv:1710.10686v1 [cs.LG]

  105. Reza M et al (2020) A survey of regularization strategies for deep models. Artif Intell Rev 53(6):3947–3986

    Google Scholar 

  106. Lu H et al (2019) The classification of mammogram using convolutional neural network with specific image preprocessing for breast cancer detection, 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD). China, Chengdu, pp 9–12

  107. Zhao X et al (2018) Classification of benign and malignant breast mass in digital mammograms with convolutional neural networks, Proceedings of the 2Nd International Symposium on Image Computing and Digital Medicine . pp 47–50

  108. Ioffe S, Christian S (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167v3 [cs.LG]

  109. Srivastava N et al (2014) Dropout: a simple way to prevent neural networks from overfitting. The J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  110. Smirnov EA et al. (2014) Comparison of regularization methods for imagenet classification with deep convolutional neural networks. Aasri Procedia 6:89–94

  111. Johnson Justin M, Khoshgoftaar Taghi M (2019) Survey on deep learning with class imbalance. J Big Data 6(1):27

    Google Scholar 

  112. Mostafizur Rahman M, Davis DN (2013) Addressing the class imbalance problem in medical datasets. Int J Mach Learn Comp 3(2):224–228

    Google Scholar 

  113. Alessandro B et al (2020) Addressing class imbalance in deep learning for small lesion detection on medical images. Comput Biol Med. 120:103735

  114. Zhang C (2019) Medical image classification under class imbalance. Graduate Theses and Dissertations. 17130. https://lib.dr.iastate.edu/etd/17130

  115. Krawczyk B (2016) Learning from imbalanced data: open challenges and future directions Prog Artif Intell. 5(4):221–232

    Google Scholar 

  116. Geert L et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Google Scholar 

  117. Erickson Bradley J et al (2017) Toolkits and libraries for deep learning. J Digit Imag 30(4):400–405

    Google Scholar 

  118. Abadi M et al (2016)TensorFlow: a system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI) pp 265–283

  119. Nikhil K (2017) Introduction to keras. Apress, Berkeley, CA, Deep learning with Python, pp 97–111

    Google Scholar 

  120. Jia Y et al (2014) Caffe: convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia. pp 675–678

  121. Paszke A et al (2017) Automatic differentiation in pytorch. 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA

  122. Yu D et al (2014) An introduction to computational networks and the computational network toolkit. Microsoft Technical Report MSR-TR-2014-112

  123. Chen TM et al (2015) A flexible and efficient machine learning library for heterogeneous distributed systems arXiv preprint arXiv:1512.01274

  124. Pillai R et al. (2019). Review of machine learning techniques in health care In: Singh P, Kar A, Singh Y, Kolekar M, Tanwar S. (eds) Proceedings of ICRIC 2019. Lecture Notes in Electrical Engineering,Springer, Cham. 597 : 103-111

  125. Liberman L, Menell JH (2002) Breast imaging reporting and data system (bi-rads). Radiol Clin 40(3):409–430

    Google Scholar 

  126. TNM and Staging of Breast Cancer Simplified, [Online]. Available: https://epomedicine.com/medical-students/tnm-staging-breast-cancer-simplified

  127. Suk HI, Shen D (2013). Deep learning-based feature representation for AD/MCI classification In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N. (eds) Medical Image Computing and Computer-Assisted Intervention MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 8150 : 583-590

  128. Oza P, Sharma P, Patel S (2021) Machine learning applications for computer-aided medical diagnostics. In: Singh PK, Wierzchoń ST, Tanwar S, Ganzha M, Rodrigues JJPC (eds) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, Springer, Singapore. vol 203. : 377-392

  129. Parita O et al (2021) A bottom-up review of image analysis methods for suspicious region detection in mammograms. J Imag 7(9):190

    Google Scholar 

  130. Oza P, Shah Y, Vegda M (2022) A comprehensive study of mammogram classification techniques. Cham, tracking and preventing diseases with artificial intelligence. Springer, pp 217–238

Download references

Funding

There is no funding involved in writing this article.

Author information

Authors and Affiliations

  1. Pandit Deendayal Energy University, Gandhinagar, India

    Parita Oza, Paawan Sharma & Samir Patel

  2. Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India

    Pankaj Kumar

Authors
  1. Parita Oza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paawan Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samir Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pankaj Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Parita Oza.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oza, P., Sharma, P., Patel, S. et al. Deep convolutional neural networks for computer-aided breast cancer diagnostic: a survey. Neural Comput & Applic 34, 1815–1836 (2022). https://doi.org/10.1007/s00521-021-06804-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06804-y

Keywords

Search

Navigation