SpringerLink
Log in

Breast mass density categorisation using deep transferred EfficientNet with support vector machines

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Breast carcinoma or breast cancer is the most common type of cancer in women. It is very dangerous, and early detection is important for proper diagnosis and reduction of mortality rate. Cancer starts in a benign state, and if there is no proper treatment, it becomes malignant. Computer-aided diagnosis (CAD) techniques can be used for early detection of cancers. These techniques can significantly and effectively help in the diagnosis of cancer. Deep learning is the most popular technique used for accurate data analysis. It is also a powerful tool for categorizing histopathological images of breast cancer and analyzing breast cell shapes and densities. This paper proposes a hybrid EfficientNet with an SVM to categorize breast mass density with consideration of tumor types. (i.e., malignant or benign). In this hybrid, EfficientNet, the mammography images are applied to EfficientNet, and then the classification is carried out using a Support Vector Machine (SVM). The proposed model achieved accuracy of 94.48%, precision of 94.79%, specificity of 99.21%, Precision of 94.79%, FPR of 0.79%, score of 94.45%, MCC of 93.78%, and Kappa of 74.76%. The model outperformed state-of-the-art techniques.

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

Access this article

Log in via an institution

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available in the KAGGLE repository, https://www.kaggle.com/datasets/ramanathansp20/inbreast-dataset.

References

  1. Ghoncheh M, Pournamdar Z, Salehiniya H (2016) Incidence and Mortality and Epidemiology of Breast Cancer in the World. Asian Pac J Cancer Prev 17(S3):43–46

    Article  PubMed  Google Scholar 

  2. DeSantis CE, Ma J, Goding SA et al (2017) Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 67(6):439–448

    Article  PubMed  Google Scholar 

  3. Kim S (2014) Margin-maximized redundancy-minimized SVMRFE for diagnostic classification of mammograms. Int J Data Min Bioinform 10(4):374–390

    Article  PubMed  Google Scholar 

  4. Pharoah PD, Sewell B, Fitzsimmons D et al (2013) Cost-effectiveness of the NHS breast screening program: life table model. BMJ 346:f2618

    Article  PubMed  PubMed Central  Google Scholar 

  5. Qiao C, Lv M, Li X, Lang X, Lv S, Long M, Feng J (2021) A Novel Human Antibody, HF, against HER2/erb-B2 Obtained by a Computer-Aided Antibody Design Method. Engineering 7(11):1566–1576. https://doi.org/10.1016/j.eng.2020.10.024

    Article  CAS  Google Scholar 

  6. Liu G-D, Li Y-C, Zhang W, Zhang L (2020) A brief review of artificial intelligence applications and algorithms for psychiatric disorders. Engineering 6(4):462–467

    Article  Google Scholar 

  7. Xu Y, Kong M, **e W, Duan R, Fang Z, Lin Y, Zhu Q, Tang S, Wu F, Yao Y-F (2021) Deep sequential feature learning in clinical image classification of infectious keratitis. Engineering 7(7):1002–1010

    Article  Google Scholar 

  8. Wang J, Wang Y, Tao X, Li Q, Sun L, Chen J, Zhou M, Hu M, Zhou X (2021) Pca-u-net based breast cancer nest segmentation from microarray hyperspectral images. Fundam Res 1(5):631–640

    Article  CAS  Google Scholar 

  9. Zhao S, Yan CY, Lv H, Yang JC, You C, Li ZA, ..., Shao ZM (2022) Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer. Fundam Res. https://doi.org/10.1016/j.fmre.2022.06.008

  10. Tang A, Tam R, Cadrin-Chênevert A et al (2018) Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Can Assoc Radiol J 69(2):120–135

    Article  PubMed  Google Scholar 

  11. Jiang F, Jiang Y, Zhi H et al (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2(4):230–243

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lawrence DR, Palacios-Gonzalez C, Harris J (2016) Artifcial Intelligence. Camb Q Healthc Ethics 25(2):250–261

    Article  PubMed  Google Scholar 

  13. Mahersia H, Boulehmi H, Hamrouni K (2016) Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: A comparative analysis. Comput Methods Programs Biomed 126:46–62

    Article  PubMed  Google Scholar 

  14. Bargalló X, Santamaría G, Del Amo M et al (2014) Single reading with computer-aided detection performed by selected radiologists in a breast cancer screening program. Eur J Radiol 83(11):2019–2023

    Article  PubMed  Google Scholar 

  15. Weiqiang Z, **angmin X, Wei H (2008) Shape, and Boundary Analysis for Classification of Breast Masses: International Symposium on Computational Intelligence & Design [C]

  16. Rangayyan RM, Mudigonda NR, Desautels JEL (2000) Boundary modeling and shape analysis methods for classification of mammographic masses. Med Biol Eng Compu 38(5):487–549

    Article  CAS  Google Scholar 

  17. Wu Y, Wei J, Hadjiiski LM et al (2007) Bilateral analysis based false positive reduction for computer-aided mass detection. Med Phys 34(8):3334–3344

    Article  PubMed  Google Scholar 

  18. Li HD, Kallergi M (1995) Markov random field for tumor detection in digital mammography. IEEE Trans Med Imaging 14(3):565–576

    Article  CAS  PubMed  Google Scholar 

  19. Sharmin S, Ahammad T, Talukder MA, Ghose P (2023) A hybrid dependable deep feature extraction and ensemble-based machine learning approach for breast cancer detection. IEEE Access. https://doi.org/10.1109/access.2023.3304628

  20. Tekin E, Yazıcı Ç, Kusetogullari H, Tokat F, Yavariabdi A, Iheme LO, ... Uzel B (2023) Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer. Sci Rep 13(1):128

  21. Li X, Keshavarz M, Kassanos P, Kidy Z, Roddan A, Yeatman E, Thompson AJ (2023) SERS Detection of Breast Cancer‐Derived Exosomes Using a Nanostructured Pt‐Black Template. Adv Sensor Res 2200039. https://doi.org/10.1002/adsr.202200039

  22. Bao C, Shen J, Zhang Y, Zhang Y, Wei W, Wang Z, Ding J, Han L (2023) Evaluation of an artificial intelligence support system for breast cancer screening in Chinese people based on mammogram. Cancer Med 12(3):3718–3726. https://doi.org/10.1002/cam4.5231

    Article  PubMed  Google Scholar 

  23. Zhang M, Li S, Xue M, Zhu Q (2023) Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning. J Biomed Opt 28(8):086002–086002

    Article  PubMed  PubMed Central  Google Scholar 

  24. Mushtaq Z, Qureshi MF, Abbass MJ, Al-Fakih SMQ (2023) Effective kernel-principal component analysis based approach for wisconsin breast cancer diagnosis. Electron Lett 59(2):e212706

    Article  Google Scholar 

  25. Patel V, Chaurasia V, Mahadeva R, Patole SP (2023) GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification. IEEE Access 11:9095–9112

    Article  Google Scholar 

  26. David S, Tran T, Dallaire F, Sheehy G, Azzi F, Trudel D, ... Meterissian S (2023) In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer. J Biomed Opt 28(3):036009–036009

  27. Mohamed TI, Ezugwu AE, Fonou-Dombeu JV, Ikotun AM, Mohammed M (2023) A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data. Sci Rep 13(1):14644

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  28. Kong X, Zhou M, Bian K, Lai W, Hu F, Dai R, Yan J (2023) Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer. Sci Rep 13(1):4386

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  29. Huang ML, Lin T-Y (2020) Dataset of breast mammography images with masses. Data in Brief 31:105928. ISSN 2352–3409. https://doi.org/10.1016/j.dib.2020.105928

  30. "INbreast Database." http://medicalresearch.inescporto.pt/breastresearch/index.php/Get_INbreast_Database. Accessed 4 Aug 2022

  31. Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS (2012) INbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236–248

    Article  PubMed  Google Scholar 

  32. Yari Y, Nguyen TV, Nguyen HT (2020) Deep Learning Applied for Histological Diagnosis of Breast Cancer. IEEE Access 8:162432–162448. https://doi.org/10.1109/ACCESS.2020.3021557

    Article  Google Scholar 

  33. Qi L, Lu X, Shen H et al (2023) Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics. J Digit Imaging. https://doi.org/10.1007/s10278-023-00798-w

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chen SW, Shivakumar SS, Dcunha S, Das J, Okon E, Qu C et al (2017) Counting apples and oranges with deep learning: A data-driven approach. IEEE Robot Autom Lett 2:781–788

    Article  CAS  Google Scholar 

  35. Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks, in International Conference on Machine Learning, pp. 6105–6114

  36. Tan M, Chen B, Pang R, Vasudevan V, Sandler M, Howard A et al (2019) Mnasnet: Platform-aware neural architecture search for mobile, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820–2828

  37. Liu H, Li B, Lv X, Huang Y (2017) Image retrieval using fused deep convolutional features. Procedia Comput Sci 107:749–754

    Article  CAS  Google Scholar 

  38. Lehman CD, Yala A, Schuster T, Dontchos B, Bahl M, Swanson K, Barzilay R (2019) Mammographic breast density assessment using deep learning: Clinical implementation. Radiology 290:52–58

    Article  PubMed  Google Scholar 

  39. Lee J, Nishikawa RM (2018) Automated mammographic breast density estimation using a fully convolutional network. Med Phys 45:1178–1190

    Article  PubMed  PubMed Central  Google Scholar 

  40. Mohamed AA, Berg WA, Peng H, Luo Y, Jankowitz RC, Wu S (2018) A deep learning method for classifying mammographic breast density categories. Med Phys 45:314–321

    Article  PubMed  Google Scholar 

  41. Dubrovina A, Kisilev P, Ginsburg B, Hashoul S, Kimmel R (2018) Computational mammography using deep neural networks. Comput Methods Biomech Biomed Eng Imaging Vis 6:243–247

    Article  Google Scholar 

  42. Gandomkar Z, Suleiman ME, Demchig D, Brennan PC, McEntee MF (2019) BI-RADS density categorization using deep neural networks. In Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment; International Society for Optics and Photonics: Bellingham WA, USA, Volume 10952, p. 109520N

  43. Saffari N, Rashwan HA, Abdel-Nasser M, Kumar Singh V, Arenas M, Mangina E, Herrera B, Puig D (2020) Fully Automated Breast Density Segmentation and Classification Using Deep Learning. Diagnostics (Basel) 10(11):988. https://doi.org/10.3390/diagnostics10110988

    Article  PubMed  Google Scholar 

  44. El Houby EMF, Nisreen IRY (2021) Malignant and nonmalignant classification of breast lesions in mammograms using convolutional neural networks. Biomed Signal Process Control 70:102954

    Article  Google Scholar 

  45. Karthiga R, Narasimhan K, Amirtharajan R (2022) Diagnosis of breast cancer for modern mammography using artificial intelligence. Math Comput Simul 202:316–330

    Article  MathSciNet  Google Scholar 

Download references

Funding

This study was not supported by any funding.

Author information

Authors and Affiliations

  1. Department of Electronics, Sambalpur University, Burla, 768019, India

    Ankita Patra, Prabira Kumar Sethy & Nalini Kanta Barpanda

  2. Department of Computer Science and Engineering, VSSUT Burla, Burla, 768018, India

    Santi Kumari Behera

  3. Department of Electronics and Communication Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur, India

    Prabira Kumar Sethy

Authors
  1. Ankita Patra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santi Kumari Behera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prabira Kumar Sethy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nalini Kanta Barpanda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Santi Kumari Behera.

Ethics declarations

Ethical approval

Not required for this study.

Consent to participate

Not required.

Consent for publication

Not applicable.

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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patra, A., Behera, S.K., Sethy, P.K. et al. Breast mass density categorisation using deep transferred EfficientNet with support vector machines. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18507-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18507-2

Keywords

Search

Navigation