Skip to main content

Advertisement

SpringerLink
Log in

Adaptive resizer-based transfer learning framework for the diagnosis of breast cancer using histopathology images

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Breast cancer is a major global health concern, and early and accurate diagnosis is crucial for effective treatment. Recent advancements in computer-assisted prediction models have facilitated diagnosis and prognosis using high-resolution histopathology images, which provide detailed information on cancerous tissue. However, these high-resolution images often require resizing, leading to potential data loss. In this study, we demonstrate the effect of a learnable adaptive resizer for breast cancer classification using the BreakHis dataset. Our approach incorporates the adaptive resizer with various convolutional neural network models, including VGG16, VGG19, MobileNetV2, InceptionResnetV2, DenseNet121, DenseNet201, and EfficientNetB0. Despite producing visually less appealing images, the learnable resizer effectively improves classification performance. DenseNet201, when jointly trained with the adaptive resizer, achieves the highest accuracy of 98.96% for input images of 448 \(\times \) 448 resolution. Our experimental results demonstrate that the adaptive resizer performs better at a magnification factor of 40\(\times \) compared to higher magnifications. While its effectiveness becomes less pronounced as image resolution increases to 100\(\times \), 200\(\times \), and 400\(\times \), the adaptive resizer still outperforms bilinear interpolation. In conclusion, this study highlights the potential of adaptive resizers in enhancing performance for medical image classification. By outperforming traditional image resizing methods, our work contributes to the advancement of deep neural networks in the field of breast cancer diagnostics.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data availability and access

The Breast Cancer Histopathological Database (BreakHis), used in this study, is publicly available at https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/.

References

  1. Weiderpass, E., Stewart, B.W.: World cancer report. The International Agency for Research on Cancer (IARC) (2020)

  2. Ahmad, S., Ullah, T., Ahmad, I., Al-Sharabi, A., Ullah, K., Khan, R.A., Rasheed, S., Ullah, I., Uddin, M., Ali, M., et al.: A novel hybrid deep learning model for metastatic cancer detection. Comput. Intell. Neurosci. 2022 (2022)

  3. Kolak, A., Kamińska, M., Sygit, K., Budny, A., Surdyka, D., Kukiełka-Budny, B., Burdan, F.: Primary and secondary prevention of breast cancer. Ann. Agric. Environ. Med. 24(4) (2017)

  4. Weigel, S., Heindel, W., Heidrich, J., Heidinger, O., Hense, H.: Reduction of advanced breast cancer stages at subsequent participation in mammography screening. In: RöFo-Fortschritte Auf dem Gebiet der Röntgenstrahlen und der Bildgebenden Verfahren, vol. 188, pp. 33–37. Georg Thieme Verlag KG (2016)

  5. Singh, S.P., Urooj, S., Lay-Ekuakille, A.: Breast cancer detection using PCPCET and ADEWNN: a geometric invariant approach to medical X-ray image sensors. IEEE Sens. J. 16(12), 4847–4855 (2016)

    Article  Google Scholar 

  6. Guo, R., Lu, G., Qin, B., Fei, B.: Ultrasound imaging technologies for breast cancer detection and management: a review. Ultrasound Med. Biol. 44(1), 37–70 (2018)

    Article  Google Scholar 

  7. Dill, T.: Contraindications to magnetic resonance imaging. Heart 94(7), 943–948 (2008)

    Article  Google Scholar 

  8. De Chiffre, L., Carmignato, S., Kruth, J.-P., Schmitt, R., Weckenmann, A.: Industrial applications of computed tomography. CIRP Ann. 63(2), 655–677 (2014)

    Article  Google Scholar 

  9. Srinidhi, C.L., Ciga, O., Martel, A.L.: Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021)

    Article  Google Scholar 

  10. Elmore, J.G., Longton, G.M., Carney, P.A., Geller, B.M., Onega, T., Tosteson, A.N., Nelson, H.D., Pepe, M.S., Allison, K.H., Schnitt, S.J.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015)

    Article  Google Scholar 

  11. Abbasniya, M.R., Sheikholeslamzadeh, S.A., Nasiri, H., Emami, S.: Classification of breast tumors based on histopathology images using deep features and ensemble of gradient boosting methods. Comput. Electr. Eng. 103, 108382 (2022)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Toğaçar, M., Özkurt, K.B., Ergen, B., Cömert, Z.: BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Physica A 545, 123592 (2020)

    Article  Google Scholar 

  15. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: Breast cancer histopathological image classification using convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2560–2567. IEEE (2016)

  16. Deniz, E., Şengür, A., Kadiroğlu, Z., Guo, Y., Bajaj, V., Budak, Ü.: Transfer learning based histopathologic image classification for breast cancer detection. Health Inf. Sci. Syst. 6, 1–7 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Zhang, A., Zheng, S., He, Y., Tan, X., Dang, H., Yuan, Q.: Mini-resizer: a minimalist learnable resizer for image geolocation. In: CIBDA 2022; 3rd International Conference on Computer Information and Big Data Applications, pp. 1–4. VDE (2022)

  19. Talebi, H., Milanfar, P.: Learning to resize images for computer vision tasks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 497–506 (2021)

  20. Presenti, A., Liang, Z., Alves Pereira, L.F., Sijbers, J., De Beenhouwer, J.: Automatic anomaly detection from X-ray images based on autoencoders. Nondestruct. Test. Eval. 37(5), 552–565 (2022)

    Article  Google Scholar 

  21. Han, X., Chen, Y.: COVID-19 classification using ct scan images with resize-MobileNet. In: 2021 International Conference on Intelligent Computing, Automation and Systems (ICICAS), pp. 286–289. IEEE (2021)

  22. Zou, L., Lam, H.F., Hu, J.: Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery. Struct. Health Monit. 14759217221122266 (2022)

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

    Article  Google Scholar 

  24. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)

    Article  Google Scholar 

  25. Nanni, L., Maguolo, G., Paci, M.: Data augmentation approaches for improving animal audio classification. Ecol. Inform. 57, 101084 (2020)

    Article  Google Scholar 

  26. Yao, X., Wang, X., Wang, S.-H., Zhang, Y.-D.: A comprehensive survey on convolutional neural network in medical image analysis. Multimedia Tools Appl. 1–45 (2020)

  27. Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., Zhang, G.: Transfer learning using computational intelligence: a survey. Knowl. Based Syst. 80, 14–23 (2015)

    Article  Google Scholar 

  28. Iman, M., Arabnia, H.R., Rasheed, K.: A review of deep transfer learning and recent advancements. Technologies 11(2), 40 (2023)

    Article  Google Scholar 

  29. Chenevert, T.L., Malyarenko, D.I., Newitt, D., Li, X., Jayatilake, M., Tudorica, A., Fedorov, A., Kikinis, R., Liu, T.T., Muzi, M.: Errors in quantitative image analysis due to platform-dependent image scaling. Transl. Oncol. 7(1), 65–71 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Izmir Institute of Technology, Gülbahçe/Urla, 35430, İzmir, Turkey

    Okan Duzyel, Mehmet Sergen Catal, Ceyhun Efe Kayan, Arda Sevinc & Abdurrahman Gumus

Authors
  1. Okan Duzyel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehmet Sergen Catal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ceyhun Efe Kayan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arda Sevinc
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abdurrahman Gumus
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, methodology, implementation, experiments, results analysis, and manuscript writing were performed by OD, MSC, and AG. CEK and AS contributed to the conceptualization of the study and manuscript review.

Corresponding author

Correspondence to Abdurrahman Gumus.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

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

Duzyel, O., Catal, M.S., Kayan, C.E. et al. Adaptive resizer-based transfer learning framework for the diagnosis of breast cancer using histopathology images. SIViP 17, 4561–4570 (2023). https://doi.org/10.1007/s11760-023-02692-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02692-y

Keywords

Search

Navigation