Advertisement

Deep convolutional network for breast cancer classification: enhanced loss function (ELF)

  • 56 Accesses

Abstract

The accurate classification of the histopathological images of breast cancer diagnosis may face a huge challenge due to the complexity of the pathologist images. Currently, computer-aided diagnosis is implemented to get sound and error-less diagnosis of this lethal disease. However, the classification accuracy and processing time can be further improved. This study was designed to control diagnosis error via enhancing image accuracy and reducing processing time by applying several algorithms such as deep learning, K-means, autoencoder in clustering and enhanced loss function (ELF) in classification. Histopathological images were obtained from five datasets and pre-processed by using stain normalisation and linear transformation filter. These images were patched in sizes of 512 × 512 and 128 × 128 and extracted to preserve the tissue and cell levels to have important information of these images. The patches were further pre-trained by ResNet50-128 and ResNet512. Meanwhile, the 128 × 128 were clustered and autoencoder was employed with K-means which used latent feature of image to obtain better clustering result. Classification algorithm is used in current proposed system to ELF. This was achieved by combining SVM loss function and optimisation problem. The current study has shown that the deep learning algorithm has increased the accuracy of breast cancer classification up to 97% compared to state-of-the-art model which gave a percentage of 95%, and the time was decreased to vary from 30 to 40 s. Also, this work has enhanced system performance via improving clustering by employing K-means with autoencoder for the nonlinear transformation of histopathological image.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Li Y, Wu J, Wu Q (2019) Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 7:21400–21408

  2. 2.

    Vo DM, Nguyen N-Q, Lee S-W (2019) Classification of breast cancer histology images using incremental boosting convolution networks. Inf Sci 482:123–138

  3. 3.

    Jadoon MM, Zhang Q, Haq IU, Butt S, Jadoon A (2017) Three-class mammogram classification based on descriptive cnn features. BioMed Res Int 3640901:11. https://doi.org/10.1155/2017/3640901

  4. 4.

    Wahab N, Khan A, Lee YS (2017) Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Comput Bio Med 85:86–97

  5. 5.

    Xie J, Liu R, Luttrell J, Zhang C (2019) Deep learning based analysis of histopathological images of breast cancer. Front Genet 10:80

  6. 6.

    Mambou SJ, Maresova P, Krejcar O, Selamat A, Kuca K (2018) breast cancer detection using infrared thermal imaging and a deep learning model, (in eng). Sensors (Basel, Switzerland) 18(9):2799

  7. 7.

    Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Programs Biomed 157:19–30

  8. 8.

    Nahid A-A, Mehrabi AM, Kong Y (2018) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. Biomed Res Int 03:07

  9. 9.

    Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International joint Conference on neural networks (IJCNN), pp 2560–2567

  10. 10.

    Roy K, Banik D, Bhattacharjee D, Nasipuri M (2019) Patch-based system for classification of breast histology images using deep learning. Comput Med Imaging Graph 71:90–103

  11. 11.

    Gandomkar Z, Brennan PC, Mello-Thoms C (2018) MuDeRN: multi-category classification of breast histopathological image using deep residual networks. Artif Intell Med 88:14–24

  12. 12.

    Zheng Y et al (2017) Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification. Pattern Recogn 71:14–25

  13. 13.

    Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197:221–231

  14. 14.

    Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 7(1):4172

  15. 15.

    Sudharshan PJ, Petitjean C, Spanhol F, Oliveira LE, Heutte L, Honeine P (2019) Multiple instance learning for histopathological breast cancer image classification. Exp Syst Appl 117:103–111

  16. 16.

    Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R (2018) Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal 47:45–67

  17. 17.

    Araújo T et al (2017) Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6):e0177544

  18. 18.

    Suzuki S, et al (2016) Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. In: 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp 1382–1386

  19. 19.

    Ragab DA, Sharkas M, Marshall S, Ren J (2019) Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ 7:e6201

  20. 20.

    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(1):314–321

  21. 21.

    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 Methods Programs Biomed 157:85–94

  22. 22.

    Asri H, Mousannif H, Moatassime HA, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci 83:1064–1069

  23. 23.

    Motlagh MH et al (2018) Breast cancer histopathological image classification: a deep learning approach. bioRxiv, p 1–8. https://doi.org/10.1109/BIBM.2018.8621307

  24. 24.

    Ting FF, Tan YJ, Sim KS (2019) Convolutional neural network improvement for breast cancer classification. Expert Syst Appl 120:103–115

  25. 25.

    Quellec G, Cazuguel G, Cochener B, Lamard M (2017) Multiple-instance learning for medical image and video analysis. IEEE Rev Biomed Eng 10:213–234

Download references

Author information

Correspondence to Abeer Alsadoon.

Ethics declarations

Conflict of interest

The authors indicate full freedom of investigation and no potential conflict of interest.

Ethical approval

Not required.

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

Verify currency and authenticity via CrossMark

Cite this article

Acharya, S., Alsadoon, A., Prasad, P.W.C. et al. Deep convolutional network for breast cancer classification: enhanced loss function (ELF). J Supercomput (2020). https://doi.org/10.1007/s11227-020-03157-6

Download citation

Keywords

  • Convolutional neural network
  • Breast cancer
  • K-means
  • Autoencoder
  • Support vector machine