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Breast Cancer Localization and Classification in Mammograms Using YoloV5

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Applications of Artificial Intelligence and Neural Systems to Data Science

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 360))

Abstract

Mammography screening is the main examination for breast cancer early detection and has shown important benefits in reducing advanced and fatal disease rates. In this paper, a YoloV5 model for simultaneous breast cancer localization and classification in mammograms was proposed. Two public datasets were used for training and testing. The CBIS-DDSM dataset, composed of scanned film mammograms, was used as a source dataset to implement the transfer learning technique on the target INbreast dataset, composed of Full-Field Digital mammograms. The Small YoloV5 model combined with a large data-augmentation strategy was the best-developed solution. An improvement of 0.103 mAP was observed with the implementation of the transfer learning techinique on the INbreast dataset. The performance was encouraging, resulting in an mAP of 0.838 ± 0.042, a recall of 0.722 ± 0.096, and a precision of 0.917 ± 0.077, calculated using the five-fold CV. The recognition rate achieved with the transfer learning on full-field digital mammograms, encouraging future analysis on a proprietary dataset.

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References

  1. Attena, F., Abagnale, L., Avitabile, A.: Online information about mammography screening in italy from 2014 to 2021. BMC Women’s Health 22(1), 1–6 (2022)

    Article  Google Scholar 

  2. Ekpo, E.U., Alakhras, M., Brennan, P.: Errors in mammography cannot be solved through technology alone. Asian Pac. J. Cancer Prev.: APJCP 19(2), 291 (2018)

    Google Scholar 

  3. Soulami, K.B., Kaabouch, N., Saidi, M.N.: Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network. Biomed. Signal Process. Control 76, 103696 (2022)

    Article  Google Scholar 

  4. Al-Masni, M.A., Al-Antari, M.A., Park, J.-M., Gi, G., Kim, T.-Y., Rivera, P., Valarezo, E., Choi, M.-T., Han, S.-M., Kim, T.-S.: Simultaneous detection and classification of breast masses in digital mammograms via a deep learning yolo-based cad system. Comput. Methods Programs Biomed. 157, 85–94 (2018)

    Article  Google Scholar 

  5. Aly, G.H., Marey, M., El-Sayed, S.A., Tolba, M.F.: Yolo based breast masses detection and classification in full-field digital mammograms. Comput. Methods Programs Biomed. 200, 105823 (2021)

    Article  Google Scholar 

  6. Baccouche, A., Garcia-Zapirain, B., Olea, C.C., Elmaghraby, A.S.: Breast lesions detection and classification via yolo-based fusion models. Comput. Mater. Contin. 69, 1407–1425 (2021)

    Google Scholar 

  7. Muduli, D., Dash, R., Majhi, B.: Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach. Biomed. Signal Process. Control 71, 102825 (2022)

    Article  Google Scholar 

  8. Mahmood, T., Li, J., Pei, Y., Akhtar, F., Rehman, M.U., Wasti, S.H.: Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach. Plos one 17(1), 0263126 (2022)

    Article  Google Scholar 

  9. Ragab, D.A., Attallah, O., Sharkas, M., Ren, J., Marshall, S.: A framework for breast cancer classification using multi-dcnns. Comput. Biol. Med. 131, 104245 (2021)

    Article  Google Scholar 

  10. Yu, X., Pang, W., Xu, Q., Liang, M.: Mammographic image classification with deep fusion learning. Sci. Rep. 10(1), 1–11 (2020)

    Google Scholar 

  11. Agarwal, R., Diaz, O., Lladó, X., Yap, M.H., Martí, R.: Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging 6(3), 031409 (2019)

    Article  Google Scholar 

  12. AlGhamdi, M., Abdel-Mottaleb, M.: Dv-dcnn: Dual-view deep convolutional neural network for matching detected masses in mammograms. Comput. Methods Programs Biomed. 207, 106152 (2021)

    Article  Google Scholar 

  13. Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4(1), 1–9 (2017)

    Article  Google Scholar 

  14. Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)

    Article  Google Scholar 

  15. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement (2018). arXiv:1804.02767

  16. Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H.: Cspnet: A new backbone that can enhance learning capability of cnn. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 390–391 (2020)

    Google Scholar 

  17. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)

    Google Scholar 

  18. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  19. Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016)

    Article  Google Scholar 

  20. Ultralytics: YoloV5 Ultralytics Github (2023). Last accessed 24 Jan 2023. https://github.com/ultralytics/yolov5

  21. wandb: Weights & Biases (2022). Last accessed 24 Jan 2023). https://github.com/wandb/wandb

  22. Agarwal, R., Díaz, O., Yap, M.H., Lladó, X., Martí, R.: Deep learning for mass detection in full field digital mammograms. Comput. Biol. Med. 121, 103774 (2020)

    Article  Google Scholar 

  23. Al-Antari, M.A., Han, S.-M., Kim, T.-S.: Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital x-ray mammograms. Comput. Methods Programs Biomed. 196, 105584 (2020)

    Article  Google Scholar 

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Acknowledgements

This work was partially supported by the University of Palermo Grant EUROSTART, CUP B79J21038330001, Project TRUSTAI4NCDI.

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Authors and Affiliations

  1. Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy

    Francesco Prinzi & Salvatore Vitabile

  2. Section of Radiology – Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University Hospital ‘Paolo Giaccone’, Palermo, Italy

    Marco Insalaco

  3. Department of Engineering, University of Palermo, Palermo, Italy

    Salvatore Gaglio

  4. ICAR-CNR, Palermo, Italy

    Salvatore Gaglio

Authors
  1. Francesco Prinzi
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  2. Marco Insalaco
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  3. Salvatore Gaglio
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  4. Salvatore Vitabile
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Corresponding author

Correspondence to Francesco Prinzi .

Editor information

Editors and Affiliations

  1. Department of Psychology, University of Campania "Luigi Vanvitelli, Caserta, Italy

    Anna Esposito

  2. Fundació Tecnocampus, Pompeu Fabra University, Mataró, Barcelona, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Energy, Environmental and Materials Engineering, University Mediterranea of Reggio Calabr, Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Prinzi, F., Insalaco, M., Gaglio, S., Vitabile, S. (2023). Breast Cancer Localization and Classification in Mammograms Using YoloV5. In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds) Applications of Artificial Intelligence and Neural Systems to Data Science. Smart Innovation, Systems and Technologies, vol 360. Springer, Singapore. https://doi.org/10.1007/978-981-99-3592-5_7

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  • DOI: https://doi.org/10.1007/978-981-99-3592-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3591-8

  • Online ISBN: 978-981-99-3592-5

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