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Assessing the Impact of a Preprocessing Stage on Deep Learning Architectures for Breast Tumor Multi-class Classification with Histopathological Images

  • Iván CalvoEmail author
  • Saul Calderon
  • Jordina Torrents-Barrena
  • Erick Muñoz
  • Domenec Puig
Conference paper
  • 25 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1087)

Abstract

In this work, we assess the impact of the adaptive unsharp mask filter as a preprocessing stage for breast tumour multi-class classification with histopathological images, evaluating two state-of-the-art architectures, not tested so far for this problem to our knowledge: DenseNet, SqueezeNet and a 5-layer baseline deep learning architecture. SqueezeNet is an efficient architecture, which can be useful in environments with restrictive computational resources. According to the results, the filter improved the accuracy from 2% to 4% in the 5-layer baseline architecture, on the other hand, DenseNet and SqueezeNet show a negative impact, losing from 2% to 6% accuracy. Hence, simpler deep learning architectures can take more advantage of filters than complex architectures, which are able to learn the preprocessing filter implemented. Squeeze net yielded the highest per parameter accuracy, while DenseNet achieved a 96% accuracy, defeating previous state of the art architectures by 1% to 5%, making DenseNet a considerably more efficient architecture for breast tumour classification.

Keywords

Breast cancer Histopathological images Deep learning Multi-class tumour classification 

References

  1. 1.
    Adeshina, S.A., Adedigba, A.P., Adeniyi, A.A., Aibinu, A.M.: Breast cancer histopathology image classification with deep convolutional neural networks. In: 2018 14th International Conference on Electronics Computer and Computation (ICECCO), pp. 206–212. IEEE (2018)Google Scholar
  2. 2.
    Benhammou, Y., Tabik, S., Achchab, B., Herrera, F.: 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, p. 47. ACM (2018)Google Scholar
  3. 3.
    Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: 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 (2018)Google Scholar
  4. 4.
    Calderon, S., et al.: Assessing the impact of the deceived non local means filter as a preprocessing stage in a convolutional neural network based approach for age estimation using digital hand x-ray images. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1752–1756. IEEE (2018)Google Scholar
  5. 5.
    Carranza-Rojas, J., Calderon-Ramirez, S., Mora-Fallas, A., Granados-Menani, M.: Unsharp masking layer: injecting prior knowledge in convolutional networks for image classification (in press)Google Scholar
  6. 6.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)Google Scholar
  7. 7.
    Dodge, S., Karam, L.: Understanding how image quality affects deep neural networks. In: 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–6. IEEE (2016)Google Scholar
  8. 8.
    Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)Google Scholar
  9. 9.
    Gandomkar, Z., Brennan, P.C., Mello-Thoms, C.: MuDeRn: multi-category classification of breast histopathological image using deep residual networks. Artif. Intell. Med. 88, 14–24 (2018)CrossRefGoogle Scholar
  10. 10.
    Gu, Y., Jie, Y.: Densely-connected multi-magnification hashing for histopathological image retrieval. IEEE J. Biomed. Health Inform. 23, 1683–1691 (2018)CrossRefGoogle Scholar
  11. 11.
    Gupta, V., Bhavsar, A.: Sequential modeling of deep features for breast cancer histopathological image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2254–2261 (2018)Google Scholar
  12. 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)CrossRefGoogle Scholar
  13. 13.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)Google Scholar
  14. 14.
    Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)
  15. 15.
    Khosravan, N., Celik, H., Turkbey, B., Jones, E.C., Wood, B., Bagci, U.: A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning. Med. Image Anal. 51, 101–115 (2019)CrossRefGoogle Scholar
  16. 16.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  17. 17.
    Lin, S., et al.: Intensity and edge based adaptive unsharp masking filter for color image enhancement. Optik 127(1), 407–414 (2016)CrossRefGoogle Scholar
  18. 18.
    Mehra, R., et al.: Breast cancer histology images classification: training from scratch or transfer learning? ICT Express 4(4), 247–254 (2018)CrossRefGoogle Scholar
  19. 19.
    Pertuz, S., Julia, C., Puig, D.: A novel mammography image representation framework with application to image registration. In: 2014 22nd International Conference on Pattern Recognition, pp. 3292–3297. IEEE (2014)Google Scholar
  20. 20.
    Polesel, A., Ramponi, G., Mathews, V.J.: Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505–510 (2000)CrossRefGoogle Scholar
  21. 21.
    Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)CrossRefGoogle Scholar
  22. 22.
    Singh, V.K., et al.: Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 833–840. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00934-2_92CrossRefGoogle Scholar
  23. 23.
    Spanhol, F.A., Oliveira, L.S., Cavalin, P.R., Petitjean, C., Heutte, L.: Deep features for breast cancer histopathological image classification. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1868–1873. IEEE (2017)Google Scholar
  24. 24.
    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 (2016)CrossRefGoogle Scholar
  25. 25.
    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)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Iván Calvo
    • 1
    Email author
  • Saul Calderon
    • 1
  • Jordina Torrents-Barrena
    • 2
  • Erick Muñoz
    • 1
  • Domenec Puig
    • 2
  1. 1.Escuela de Computación, Tecnológico de Costa RicaSan JoseCosta Rica
  2. 2.Dep. d’Enginyeria Informàtica i MatemàtiquesUniversitat Rovira i VirgiliTarragonaSpain

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