Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

GLNET: global–local CNN's-based informed model for detection of breast cancer categories from histopathological slides

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In computer vision, particularly in label categorization, attributing features such as color, shape, and tissue size to each category presents a formidable challenge. Dense features related to each category have been validated in recent studies and developed as a multi-label classification problem. Still, notable difficulties remain in (1) classifying attributes more extensively over different object categories, (2) correlating category vulnerability, (3) capturing features in one way, and (4) predicting category labels of a slide with a dense feature map. We have proposed a pre-trained ResNet101-based novel global–local convolution technique to resolve these issues. The proposed model has used ResNet101 as a backbone with additional convolutional, regularization, and dense layers. This technique has two methods to extract the most contributed histopathological slide features. The global descriptor has helped the model to identify the WSI global feature WGF(color, shape, tissue size). In contrast, the local feature extractor has WLF, which fuses the region of interest toward the slides category. After that, we combined WGL features (WSI to patch3×4) as an extension of the informed model to learn dense features of multi-label breast cancer categories. After that, the GLNET model uses a fine-tuning mechanism with informed learned to different categories of faded dense layers. Generally, the global–local blocks make sense of the WSI global feature while gaining the object-of-interest characteristic. The proposed model has used the global–local feature composition for each category of breast cancer. Our proposed model has improved accuracy on two benchmarks and challenging BreakHis and ICIAR2018-BachChallenge datasets for multi-label cancer category prediction. The model results stated in different evaluation matrices verify that the proposed model gains 2% accuracy compared to the existing classifier. Finally, through a series of experiments, we have demonstrated that the proposed model significantly improves accuracy in training on histopathological slides characterized by their complex nature.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

Similar content being viewed by others

Data availability

Dataset is publicly available.

References

  1. American cancer society.about breast cancer.org—1.800.227.2345. https://www.cancer.org/content/dam/CRC/PDF/Public/8577.00.pdf

  2. Deniz ES, Engür A, Kadiroglu Z, Guo Y, Bajaj V, Budak Ü (2018) Transfer learning based histopathologic image classification for breast cancer detection. Health Inf Sci Syst 6(1):18

    Article  Google Scholar 

  3. Das K, Conjeti S, Roy AG, Chatterjee J, Sheet D (2018) Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, pp 578–581

  4. Li L et al (2020) Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images. Multimed. Tools Appl. 79(21):14509–14528

    Article  Google Scholar 

  5. Ahmad N, Asghar S, Gillani SA (2022) Transfer learning-assisted multi-resolution breast cancer histopathological images classification. Vis. Comput. 38(8):2751–2770

    Article  Google Scholar 

  6. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5):1207–1216

    Article  Google Scholar 

  7. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778

  8. Howard A, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR, arXiv:1704.04861

  9. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations

  10. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–9

  11. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2818–2826

  12. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4510–4520

  13. Tajbakhsh N, Shin JY, Gurudu SR, Todd Hurst R, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312

    Article  Google Scholar 

  14. Mehra R et al (2018) Breast cancer histology images classification: training from scratch or transfer learning? ICT Exp 4(4):247–254

    Article  Google Scholar 

  15. Kablan EB, Dogan H, Ercin ME, Ersoz S, Ekinci M (2020) Anensemble of fine-tuned fully convolutional neural networks for pleural effusion cell nuclei segmentation. Comput Electr Eng 81:106533

    Article  Google Scholar 

  16. Akhtar Z, Foresti GL (2016) Face spoof attack recognition using discriminative image patches. J Electr Comput Eng 2016:66

    Google Scholar 

  17. Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn Lett 125:1–6

    Article  Google Scholar 

  18. Chan A, Tuszynski JA (2016) Automatic prediction of tumour malignancy in breast cancer with fractal dimension. R Soc Open Sci 3(12):160558

    Article  Google Scholar 

  19. Nawaz MA, Sewissy AA, Soliman THA (2018) Automated classification of breast cancer histology images using deep learning based convolutional neural networks. Int J Comput Sci Netw Secur 4:152–160

    Google Scholar 

  20. Mormont R, Geurts P, Maree R (2020) Multi-task pre-training ´ of deep neural networks for digital pathology. IEEE J Biomed Health Inform 6:66

    Google Scholar 

  21. Medela A, Picon A, Saratxaga CL, Belar O, Cabezon V, Cicchi R, Bilbao R, Glover B (2019) Few shot learning in histopathological images: reducing the need of labeled data on biological datasets. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, pp 1860–1864

  22. Samah AA, Fauzi MFA, Mansor S (2017) Classification of benign and malignant tumors in histopathology images. In: 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, pp 102–106

  23. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2015) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455–1462

    Article  Google Scholar 

  24. Kahya MA, Al-Hayani W, Algamal ZY (2017) Classification of breast cancer histopathology images based on adaptive sparse support vector machine. J Appl Math Bioinform 7(1):49

    Google Scholar 

  25. Sanchez-Morillo D, González J, García-Rojo M, Ortega J (2018) Classification of breast cancer histopathological images using kaze features. In: International Conference on Bioinformatics and Biomedical Engineering. Springer, Berlin, pp 276–286

  26. Nahid A-A, Mehrabi MA, Kong Y (20185) Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed Res Int 6, 66

  27. Jiang Y, Chen L, Zhang H, Xiao X (2019) Breast cancer histopathological image classification using convolutional neural networks with small se-resnet module. PLoS ONE 14(3):e0214587

    Article  Google Scholar 

  28. Nejad EM, Affendey LS, Latip RB, Ishak IB (2017) Classification of histopathology images of breast into benign and malignant using a single-layer convolutional neural network. In: Proceedings of the International Conference on Imaging, Signal Processing and Communication, pp 50–53

  29. Kumar K, Rao ACS (2018) Breast cancer classification of image using convolutional neural network. In: 2018 4th International Conference on Recent Advances in Information Technology (RAIT). IEEE, pp 1–6

  30. Sun J, Binder A (2017) Comparison of deep learning architectures for H&E histopathology images. In: 2017 IEEE Conference on Big Data and Analytics (ICBDA). IEEE, pp 43–48

  31. Benhammou Y, Tabik S, Achchab B, Herrera F (2018) A first study exploring the performance of the state-of-the art cnn model in the problem of breast cancer. In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, pp 1–6

  32. Sharma S, Mehra R (2020) Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images-a comparative insight. J Digit Imaging 33(3):632–654

    Article  Google Scholar 

  33. Bakkouri I et al (2022) BG-3DM2F: Bidirectional gated 3D multi-scale feature fusion for Alzheimer’s disease diagnosis. Multimed Tools Appl 81(8):10743–10776

    Article  Google Scholar 

  34. Bakkouri I, Afdel K (2023) MLCA2F: multi-level context attentional feature fusion for COVID-19 lesion segmentation from CT scans. Signal Image Video Process 17(4):1181–1188

    Article  Google Scholar 

  35. Alom MZ et al (2019) Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imaging 32:605–617

    Article  Google Scholar 

  36. Bhatt D et al (2021) CNN variants for computer vision: history, architecture, application, challenges and future scope. Electronics 10(20):2470

    Article  Google Scholar 

  37. Patel C et al (2022) DBGC: dimension-based generic convolution block for object recognition. Sensors 22(5):1780

    Article  Google Scholar 

  38. Guo Y et al (2020) DeepANF: a deep attentive neural framework with distributed representation for chromatin accessibility prediction. Neurocomputing 379:305–318

    Article  Google Scholar 

  39. Singh J et al (2019) RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nat Commun 10(1):1–13

    Article  Google Scholar 

  40. Guo Y et al (2019) DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction. BMC Bioinform 20(1):1–12

    Article  MathSciNet  Google Scholar 

  41. Gao Q, Lim S, Jia X (2019) Spectral–spatial hyperspectral image classification using a multiscale conservative smoothing scheme and adaptive sparse representation. IEEE Trans Geosci Remote Sens 57(10):7718–7730

    Article  Google Scholar 

  42. Macenko M et al (2009) A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE

  43. Metwaly K et al (2022) Glidenet: global, local and intrinsic based dense embedding network for multi-category attributes prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

  44. Sergey I, Christian S (202s1) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv 2015. arXiv preprint arXiv:1502.03167

  45. Kassani SH et al (2019) Breast cancer diagnosis with transfer learning and global pooling. In: 2019 International Conference on Information and Communication Technology Convergence (ICTC). IEEE

  46. Al-Ameen Z, Muttar A, Al-Badrani G (2019) Improving the sharpness of digital image using an amended unsharp mask filter. Int J Image Graph Signal Process 11(3):66

    Google Scholar 

  47. Boumaraf S et al (2021) Conventional machine learning versus deep learning for magnification dependent histopathological breast cancer image classification: a comparative study with visual explanation. Diagnostics 11(3):528

    Article  Google Scholar 

  48. Bardou D, Zhang K, Ahmad SM (2018) Classification of breast cancer based on histology images using convolutional neural networks. IEEE Access 6:24680–24693

    Article  Google Scholar 

  49. Spanhol FA et al (2016) Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE

  50. Gour M, Jain S, Kumar TS (2020) Residual learning-based CNN for breast cancer histopathological image classification. Int J Imaging Syst Technol 30(3):621–635

    Article  Google Scholar 

  51. Singh S, Kumar R (2022) Breast cancer detection from histopathology images with deep inception and residual blocks. Multimed Tools Appl 81(4):5849–5865

    Article  Google Scholar 

  52. Laxmisagar HS., Hanumantharaju MC (2021) Design of an efficient deep neural network for multi-level classification of breast cancer histology images. In: Intelligent Computing and Applications. Springer, Singapore, pp 447–459

  53. Aresta G et al (2019) Bach: grand challenge on breast cancer histology images. Med Image Anal 56:122–139

    Article  Google Scholar 

  54. Golatkar A, Anand D, Sethi A (2018) Classification of breast cancer histology using deep learning. In: International Conference Image Analysis and Recognition, pp 837–844. https://doi.org/10.1007/978-3-319-93000-8_95

  55. Yan R et al (2020) Breast cancer histopathological image classification using a hybrid deep neural network. Methods 173:52–60. https://doi.org/10.1016/j.ymeth.2019.06.014

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2020JJ4757). This work is supported by the Intelligent annotation and fine-grained recognition of large-scale multimodal medical behavior belong to 2030 Innovation Megaprojects (to be fully launched by 2020)-New Generation Artificial Intelligence (Project No. 2020AAA0109600). This work is funded by the National Key R&D Program of China Under Grant 2021ZD0140301, the National Natural Science Foundation of China under Project No. 61902433, and the High-Performance Computing Center of Central South University.

Funding

No funding.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Saif Ur Rehman Khan, Ming Zhao, Sohaib Asif & Xuehan Chen

  2. School of Mathematics, Hunan University, Changsha, China

    Yusen Zhu

Authors
  1. Saif Ur Rehman Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sohaib Asif
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuehan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yusen Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Saif Ur Rehman Khan was involved in Methodology, Formal analysis, Validation, and Writing—original draft. Ming Zhao contributed to Conceptualization, Formal analysis, Supervision, and Writing—review & editing. Sohaib Asif was involved in Conceptualization, Methodology, Formal analysis, and Writing—review & editing. Xuehan Chen contributed to Conceptualization, Formal analysis, Supervision, and Writing—review & editing. Yusen Zhu was involved in Formal analysis and Validation. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ming Zhao.

Ethics declarations

Conflict of interest

There is no conflict of interest between authors.

Ethics approval

Approved.

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

Khan, S.U.R., Zhao, M., Asif, S. et al. GLNET: global–local CNN's-based informed model for detection of breast cancer categories from histopathological slides. J Supercomput 80, 7316–7348 (2024). https://doi.org/10.1007/s11227-023-05742-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05742-x

Keywords

Search

Navigation