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Breast lesions segmentation and classification in a two-stage process based on Mask-RCNN and Transfer Learning

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Abstract

The most prevalent malignancy of concern among women is breast cancer. Early detection plays a crucial role in improving survival chances. However, the current reliance on mammography for breast cancer detection has limitations due to the delicate nature of breast tissue, human visual system constraints, and variations in accumulated experience, leading to significant false positive and false negative results. This study aims to minimize these errors by developing an intelligent computer-based diagnosis method for breast cancer utilizing digital mammography, employing the Transfer Learning approach. The proposed technique involves two stages. In the first stage, we fine-tune the pre-trained Mask R-CNN model on the COCO dataset to identify and segment breast lesions. In the second stage, various convolutional Deep Learning models such as ResNet101, ResNet34, VGG16, VGG19, AlexNet, and DenseNet121 classify the segmented lesions as benign or malignant. The lesion detection and segmentation stages achieved an average precision of 96.26%, while breast lesion classification using the DenseNet121 model obtained 99.44% accuracy on the INbreast dataset. An additional benefit of this study is the development of a new dataset extracted from the INbreast dataset, containing solely lesion images. This novel dataset reduces storage capacity and computational complexity during Deep neural network training and testing as it avoids the use of entire images. Moreover, the lesion dataset holds potential for use in breast cancer diagnosis research and may be integrated into an advanced computer-assisted diagnostic system for breast cancer screening.

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Data availability

The data used on this paper are available at: https://doi.org/10.1016/j.acra.2011.09.014

References

  1. Basu AK (2018) DNA Damage, Mutagenesis and Cancer. Int J Mol Sci 19:970. https://doi.org/10.3390/ijms19040970

    Article  Google Scholar 

  2. Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer Statistics, 2021. CA Cancer J Clin 71:7–33. https://doi.org/10.3322/caac.21654

    Article  Google Scholar 

  3. Maxim LD, Niebo R, Utell MJ (2014) Screening tests: a review with examples. Inhal Toxicol 26:811–828. https://doi.org/10.3109/08958378.2014.955932

    Article  Google Scholar 

  4. Guo Z, Xie J, Wan Y et al (2022) A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis. Open Life Sci 17:1600–1611

    Article  Google Scholar 

  5. Usang EE, Maram A, Patrick B (2018) Errors in Mammography Cannot be Solved Through Technology Alone. Asian Pac J Cancer Prev 19:291–301. https://doi.org/10.22034/APJCP.2018.19.2.291

    Article  Google Scholar 

  6. Abdelhafiz D, Bi J, Ammar R et al (2020) Convolutional neural network for automated mass segmentation in mammography. BMC Bioinformatics 21:192. https://doi.org/10.1186/s12859-020-3521-y

    Article  Google Scholar 

  7. Hamed G, Marey M, Amin SE, Tolba MF (2021) Automated Breast Cancer Detection and Classification in Full Field Digital Mammograms Using Two Full and Cropped Detection Paths Approach. IEEE Access 9:116898–116913. https://doi.org/10.1109/ACCESS.2021.3105924

    Article  Google Scholar 

  8. Al-Antari MA, Al-Masni MA, Kim T-S (2020) Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram. Adv Exp Med Biol 59–72. https://doi.org/10.1007/978-3-030-33128-3_4

  9. Soltani H, Amroune M, Bendib I, Haouam MY (2021) Breast Cancer Lesion Detection and Segmentation Based On Mask R-CNN. In: 2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI). IEEE, pp 1–6

  10. Chakraborty A, Kumer D, Deeba K (2021) Plant Leaf Disease Recognition Using Fastai Image Classification. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). pp 1624–1630

  11. Jung H, Kim B, Lee I et al (2018) Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS One 13:e0203355. https://doi.org/10.1371/journal.pone.0203355

    Article  Google Scholar 

  12. Hekal AA, Elnakib A, Moustafa HE-D (2021) Automated early breast cancer detection and classification system. Signal Image Video Process 15:1497–1505. https://doi.org/10.1007/s11760-021-01882-w

    Article  Google Scholar 

  13. Hassan NM, Hamad S, Mahar K (2022) Mammogram breast cancer CAD systems for mass detection and classification: a review. Multimed Tools Appl 81:20043–20075. https://doi.org/10.1007/s11042-022-12332-1

    Article  Google Scholar 

  14. Sheba KU, Gladston Raj S (2018) An approach for automatic lesion detection in mammograms. Cogent Eng 5:1444320. https://doi.org/10.1080/23311916.2018.1444320

    Article  Google Scholar 

  15. Zhu Z, Wang S-H, Zhang Y-D (2023) A Survey of Convolutional Neural Network in Breast Cancer. Comput Model Eng Sci 136:2127–2172. https://doi.org/10.32604/cmes.2023.025484

    Article  Google Scholar 

  16. Lévy D, Jain A (2016) Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks, ArXiv abs/1612.00542.

  17. Jalloul R, Chethan HK, Alkhatib R (2023) A Review of Machine Learning Techniques for the Classification and Detection of Breast Cancer from Medical Images. Diagnostics 13:2460. https://doi.org/10.3390/diagnostics13142460

    Article  Google Scholar 

  18. Al-antari MA, Al-masni MA, Choi M-T et al (2018) A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform 117:44–54. https://doi.org/10.1016/j.ijmedinf.2018.06.003

    Article  Google Scholar 

  19. Agarwal R, Díaz O, Yap MH et al (2020) Deep learning for mass detection in Full Field Digital Mammograms. Comput Biol Med 121:103774. https://doi.org/10.1016/j.compbiomed.2020.103774

    Article  Google Scholar 

  20. Michael E, Ma H, Li H et al (2021) Breast Cancer Segmentation Methods: Current Status and Future Potentials. Biomed Res Int 2021:1–29. https://doi.org/10.1155/2021/9962109

    Article  Google Scholar 

  21. Ahmed L, Iqbal MM, Aldabbas H et al (2020) Images data practices for Semantic Segmentation of Breast Cancer using Deep Neural Network. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01680-1

    Article  Google Scholar 

  22. Baccouche A, Garcia-Zapirain B, Castillo Olea C, Elmaghraby AS (2021) Connected-UNets: a deep learning architecture for breast mass segmentation. NPJ Breast Cancer 7:151. https://doi.org/10.1038/s41523-021-00358-x

    Article  Google Scholar 

  23. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, Los Alamitos, CA, USA, pp 2980–2988

    Chapter  Google Scholar 

  24. Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3: https://doi.org/10.1186/s40537-016-0043-6

  25. Howard J, Gugger S (2020) Fastai: A Layered API for Deep Learning. Information 11:108. https://doi.org/10.3390/info11020108

    Article  Google Scholar 

  26. Moreira IC, Amaral I, Domingues I et al (2012) INbreast. Acad Radiol 19:236–248. https://doi.org/10.1016/j.acra.2011.09.014

    Article  Google Scholar 

  27. Wu Y, Kirillov A, Massa F et al (2019) Detectron2, https://github.com/facebookresearch/detectron2. Accessed 14 Dec 2022

  28. Casado-Garcia A, Dominguez C, Garcia-Dominguez M, et al (2019) CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks. BMC Bioinformatics 20: https://doi.org/10.1186/s12859-019-2931-1

Download references

Acknowledgements

This work is related to a research project of a mixed team of researchers titled “Intelligence Artificielle au Service de la detection du cancer du sein” at the Echahid Cheikh Laarbi tebessi University, Tebessa-Algeria.

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Authors and Affiliations

  1. LAMIS Laboratory, Echahid Cheikh Laarbi Tbessi University, Tebessa, Algeria

    Hama Soltani, Mohamed Amroune, Issam Bendib & Mohamed-Yassine Haouam

  2. Staffordshire University, Stoke-On-Trent, UK

    Elhadj Benkhelifa

  3. National University of Science and Technology (NUST), Islamabad, Pakistan

    Muhammad Moazam Fraz

  4. The Alan Turing Institute, 96 Euston Road, London, NW12DB, UK

    Muhammad Moazam Fraz

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  1. Hama Soltani
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  2. Mohamed Amroune
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  4. Mohamed-Yassine Haouam
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  5. Elhadj Benkhelifa
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  6. Muhammad Moazam Fraz
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Correspondence to Hama Soltani.

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Soltani, H., Amroune, M., Bendib, I. et al. Breast lesions segmentation and classification in a two-stage process based on Mask-RCNN and Transfer Learning. Multimed Tools Appl 83, 35763–35780 (2024). https://doi.org/10.1007/s11042-023-16895-5

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