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A Dual-Stage Noise Training Scheme for Breast Ultrasound Image Classification

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1842))

Abstract

Breast cancer is one of the most common types of cancer and among the top leading causes of death among women all around the world. An early diagnosis is extremely critical, and in-time treatments can greatly help to prevent the cancer cells from spreading. Mammography, MRI, biopsy, ultrasound, etc., are recognized as effective imaging tests for breast cancer. However, the diagnosis entirely depends on the experience and expertise of the radiologist. In the last decade, computer-aided diagnosis (CAD) systems have been developed as a secondary reference to provide an objective analysis of medical images. With the help of deep learning (DL) and convolutional neural networks (CNNs), the accuracy of intelligent systems on image tasks has been perpetually improved. In this work, we specifically focus on the breast ultrasound image classification task. We apply transfer learning to four widely used backbone CNN architectures in the medical image classification field: AlexNet, ResNet-18, ResNet-50, and VGG16. They are fine-tuned on carefully constructed noisy datasets, and the test results suggest that they all acquire remarkable noise resistance, and this immunity is almost invariant to noise intensity. We systematically formalize our methodology as a dual-stage noise training scheme and provide empirical parameter configurations for each backbone CNN. This scheme enjoys being simple, effective, and universal. We believe this study will benefit the development of the robust design of DL models in the medical area.

This work was partially supported by the Philip and Virginia Sproul Professorship.

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Notes

  1. 1.

    https://github.com/YimingBian/Speckle_noise_IC.

References

  1. Young, J.: SEER summary staging manual 2000: codes and coding instructions. National Cancer Institute, National Institutes of Health (2001)

    Google Scholar 

  2. Wang, Y., Ge, X., Ma, H., Qi, S., Zhang, G., Yao, Y.: Deep learning in medical ultrasound image analysis: a review. IEEE Access 9, 54310–54324 (2021)

    Article  Google Scholar 

  3. Liu, S., et al.: Deep learning in medical ultrasound analysis: a review. Engineering 5, 261–275 (2019)

    Article  Google Scholar 

  4. Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  5. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115, 211–252 (2015)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  7. Bian, Y., Somani, A.: An effective two-stage noise training methodology for classification of breast ultrasound images. In: Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, pp. 83–94 (2022)

    Google Scholar 

  8. Kim, H., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M., Ganslandt, T.: Transfer learning for medical image classification: a literature review. BMC Med. Imaging 22, 1–13 (2022)

    Article  Google Scholar 

  9. Ayana, G., Park, J., Jeong, J., Choe, S.: A novel multistage transfer learning for ultrasound breast cancer image classification. Diagnostics 12, 135 (2022)

    Article  Google Scholar 

  10. Wang, X., et al.: UD-MIL: uncertainty-driven deep multiple instance learning for OCT image classification. IEEE J. Biomed. Health Inform. 24, 3431–3442 (2020)

    Article  Google Scholar 

  11. Bhateja, V., Srivastava, A., Singh, G., Singh, J.: A modified speckle suppression algorithm for breast ultrasound images using directional filters. In: ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol II, pp. 219–226 (2014)

    Google Scholar 

  12. Li, X., Wang, Y., Zhao, Y., Wei, Y.: Fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional deep learning. Front. Physiol. 13, 698 (2022)

    Google Scholar 

  13. Tan, M., Le, Q.: Efficientnetv2: smaller models and faster training. In: International Conference on Machine Learning, pp. 10096–10106 (2021)

    Google Scholar 

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

    Google Scholar 

  15. Sezer, A., Sezer, H.: Deep convolutional neural network-based automatic classification of neonatal hip ultrasound images: a novel data augmentation approach with speckle noise reduction. Ultrasound Med. Biol. 46, 735–749 (2020)

    Article  Google Scholar 

  16. Sudharson, S., Kokil, P.: An ensemble of deep neural networks for kidney ultrasound image classification. Comput. Methods Programs Biomed. 197, 105709 (2020)

    Article  Google Scholar 

  17. Dainty, J.: Laser Speckle and Related Phenomena. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-662-43205-1

    Book  Google Scholar 

  18. Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)

    Article  Google Scholar 

  19. Rasham, N., Abbas, H., Abdul Razaq, A., Mohamad, H.: Simulation of speckle noise using image processing techniques. In: Computer Networks and Inventive Communication Technologies, pp. 489–501 (2022)

    Google Scholar 

  20. Badawy, S., Mohamed, A., Hefnawy, A., Zidan, H., GadAllah, M., El-Banby, G.: Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning-a feasibility study. PLoS ONE 16, e0251899 (2021)

    Article  Google Scholar 

  21. Buda, M., Maki, A., Mazurowski, M.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018)

    Article  Google Scholar 

  22. Ling, C., Li, C.: Data mining for direct marketing: problems and solutions. In: KDD 1998, pp. 73–79 (1998)

    Google Scholar 

  23. Nawaz, W., Ahmed, S., Tahir, A., Khan, H.: Classification of breast cancer histology images using ALEXNET. In: International Conference Image Analysis and Recognition, pp. 869–876 (2018)

    Google Scholar 

  24. Masud, M., et al.: Pre-trained convolutional neural networks for breast cancer detection using ultrasound images. ACM Trans. Internet Technol. (TOIT) 21, 1–17 (2021)

    Google Scholar 

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

    Article  Google Scholar 

  26. Al-Haija, Q., Adebanjo, A.: Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network. In: 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–7 (2020)

    Google Scholar 

  27. Virmani, J., Agarwal, R., et al.: Deep feature extraction and classification of breast ultrasound images. Multimedia Tools Appl. 79, 27257–27292 (2020)

    Article  Google Scholar 

  28. Yap, M., et al.: Breast ultrasound region of interest detection and lesion localisation. Artif. Intell. Med. 107, 101880 (2020)

    Article  Google Scholar 

  29. Moon, W., Lee, Y., Ke, H., Lee, S., Huang, C., Chang, R.: Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Comput. Methods Programs Biomed. 190, 105361 (2020)

    Article  Google Scholar 

  30. Jahangeer, G., Rajkumar, T.: Early detection of breast cancer using hybrid of series network and VGG-16. Multimedia Tools Appl. 80, 7853–7886 (2021)

    Article  Google Scholar 

  31. Albashish, D., Al-Sayyed, R., Abdullah, A., Ryalat, M., Almansour, N.: Deep CNN model based on VGG16 for breast cancer classification. In: 2021 International Conference on Information Technology (ICIT), pp. 805–810 (2021)

    Google Scholar 

  32. Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)

    Google Scholar 

  33. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:1409.1556 (2014)

  34. Taha, A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15, 1–28 (2015)

    Article  Google Scholar 

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Acknowledgements

The research reported in this paper in part was funded by the Philip and Virginia Sproul Professorship at Iowa State University. The computing for this research was supported by HPC@ISU equipment mostly purchased through funding provided by the NSF grants numbers MRI 1726447 and MRI 2018594. All opinions, findings, and conclusions expressed are those of the authors.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, 50010, USA

    Yiming Bian & Arun K. Somani

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  1. Yiming Bian
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  2. Arun K. Somani
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Corresponding author

Correspondence to Yiming Bian .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Frans Coenen

  2. Instituto de Telecomunicações and University of Lisbon, Lisbon, Portugal

    Ana Fred

  3. Universidade da Madeira and Madeira-ITI, Funchal, Portugal

    David Aveiro

  4. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  5. Polytechnic Institute of Coimbra - ISEC, Coimbra, Portugal

    Jorge Bernardino

  6. University of Naples Federico II, Naples, Italy

    Elio Masciari

  7. Polytechnic Institute of Setúbal and INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Bian, Y., Somani, A.K. (2023). A Dual-Stage Noise Training Scheme for Breast Ultrasound Image Classification. In: Coenen, F., et al. Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2022. Communications in Computer and Information Science, vol 1842. Springer, Cham. https://doi.org/10.1007/978-3-031-43471-6_3

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  • DOI: https://doi.org/10.1007/978-3-031-43471-6_3

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