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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1166))

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Abstract

The early detection of breast tumors is a critical concern for healthcare professionals, including oncologists and radiologists. While Artificial Intelligence (AI) has demonstrated potential in early breast cancer diagnosis, the efficacy of these models is often constrained by the limited size and lack of diversity in medical training sets. Although data augmentation techniques are explored to enlarge and enhance training sets, many such methods neglect the crucial aspect of sample diversity, leading to suboptimal tumor identification. Among the prevalent data augmentation techniques, the MixUp method is commonly employed to increase the size and diversity of data sets. However, its application in ultrasound image enhancement can introduce extraneous noise and may result in the loss of vital image features. This paper presents a novel data augmentation strategy termed Cluster and MixUP (Cluster MixUP) Augmentation, designed to enrich the diversity of training data while retaining essential image features. The approach combines K-means clustering with the MixUp Augmentation technique to group and mix images effectively. The efficacy of the proposed strategy is validated using the Breast Ultrasound Images database (BUSI), demonstrating superior performance and generalizability in breast cancer detection relative to existing data augmentation methods.

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References

  1. Agrawal, T., Choudhary, P.: Segmentation and classification on chest radiography: a systematic survey. Vis. Comput. 39(3), 875–913 (2023)

    Article  Google Scholar 

  2. Al-Dhabyani, W., Gomaa, M., Khaled, H., Aly, F.: Deep learning approaches for data augmentation and classification of breast masses using ultrasound images. Int. J. Adv. Comput. Sci. Appl. 10(5), 1–11 (2019)

    Google Scholar 

  3. Alblwi, A., Baksh, M., Barner, K.E.: Bone age assessment based on salient object segmentation. In: 2021 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–5. IEEE (2021)

    Google Scholar 

  4. Alblwi, A., Barner, K.E.: Optimizing feature representation via a nested network for object segmentation. In: 2022 8th International Conference on Optimization and Applications (ICOA), pp. 1–6. IEEE (2022)

    Google Scholar 

  5. Alblwi, A., Barner, K.E.: Ultrasound image segmentation via multi-scale salient network (2024), under submission

    Google Scholar 

  6. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  7. Cao, W., Chen, H.D., Yu, Y.W., Li, N., Chen, W.Q.: Changing profiles of cancer burden worldwide and in china: a secondary analysis of the global cancer statistics 2020. Chin. Med. J. 134(07), 783–791 (2021)

    Article  Google Scholar 

  8. Chen, C., Chuah, J.H., Ali, R., Wang, Y.: Retinal vessel segmentation using deep learning: a review. IEEE Access 9, 111985–112004 (2021)

    Article  Google Scholar 

  9. Dai, P., Dong, L., Zhang, R., Zhu, H., Wu, J., Yuan, K.: Soft-cp: a credible and effective data augmentation for semantic segmentation of medical lesions. arXiv preprint arXiv:2203.10507 (2022)

  10. Fahad Ullah, M.: Breast cancer: current perspectives on the disease status. In: Breast Cancer Metastasis and Drug Resistance: Challenges and Progress, pp. 51–64 (2019)

    Google Scholar 

  11. Li, J.P.O., et al.: Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog. Retin. Eye Res. 82, 100900 (2021)

    Google Scholar 

  12. Michael, E., Ma, H., Li, H., Kulwa, F., Li, J.: Breast cancer segmentation methods: current status and future potentials. Biomed. Res. Int. 2021, 1–29 (2021)

    Article  Google Scholar 

  13. Nalepa, J., Marcinkiewicz, M., Kawulok, M.: Data augmentation for brain-tumor segmentation: a review. Front. Comput. Neurosci. 13, 83 (2019)

    Article  Google Scholar 

  14. Nemoto, T., et al.: Effects of sample size and data augmentation on u-net-based automatic segmentation of various organs. Radiol. Phys. Technol. 14, 318–327 (2021)

    Google Scholar 

  15. Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

  16. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

  17. Sims, R., et al.: A pre-clinical assessment of an atlas-based automatic segmentation tool for the head and neck. Radiother. Oncol. 93(3), 474–478 (2009)

    Google Scholar 

  18. Sun, X., et al.: Robust retinal vessel segmentation from a data augmentation perspective. In: Fu, H., et al. (eds.) Ophthalmic Medical Image Analysis. OMIA 2021. LNCS, vol. 12970, pp. 189–198. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87000-3_20

  19. Wang, Y., Ji, Y., Xiao, H.: A data augmentation method for fully automatic brain tumor segmentation. Comput. Biol. Med. 149, 106039 (2022)

    Article  Google Scholar 

  20. Zhang, H., Cissé, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)

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Correspondence to Abdalrahman Alblwi .

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Alblwi, A., Mehmood, N., Labombard, J., Barner, K.E. (2024). A Data Augmentation Approach to Enhance Breast Cancer Segmentation. In: Su, R., Zhang, YD., Frangi, A.F. (eds) Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-97-1335-6_14

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  • DOI: https://doi.org/10.1007/978-981-97-1335-6_14

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  • Online ISBN: 978-981-97-1335-6

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