Downloads

https://doi.org/10.53941/aim.2024.100005
Wang, Y., Zhao, Q., Zhang, B., Tian, D., Zhang, R., & Zhong, W. A Comparative Study of Deep Learning in Breast Ultrasound Lesion Detection: From Two-Stage to One-Stage, from Anchor-Based to Anchor-Free. AI Medicine. 2024. doi: https://doi.org/10.53941/aim.2024.100005
Forthcoming Issue
Copyright (c) 2024 by the authors.
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Article

A Comparative Study of Deep Learning in Breast Ultrasound Lesion Detection: From Two-Stage to One-Stage, from Anchor-Based to Anchor-Free

Yu Wang 1, Qi Zhao 1, Baihua Zhang 2, Dingcheng Tian 1, Ruyi Zhang 1 and Wan Zhong 3,∗

1 College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110024 , China

2 Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo 315000, China

3 General Hospital of Northern Theater Command, Shenyang 110024, China

∗ Correspondence: wzhong_88@163.com

Received: 16 July 2024; Revised: 26 August 2024; Accepted: 27 August 2024; Published: 4 September 2024

 

Abstract: Breast cancer is one of the most common tumors among women in the world, and its early screening is crucial to improve the survival rate of patients. Breast ultrasound, with the characteristics of non radiation, real-time imaging and easy operation, has become a common method for breast cancer detection. However, this method has some problems, such as low imaging quality and strong subjectivity of diagnosis results, which affect the accurate diagnosis of breast cancer. With the ongoing advancement of deep learning technology, intelligent breast cancer detection systems have effectively overcome these challenges, enhancing diagnostic accuracy and efficiency. This study uses nine popular deep learning object detection networks (including two-stage, one-stage, anchor-based, and anchor-free networks) for the detection of breast lesions and compares the results of these methods. The experiments show that the anchor-based Single Shot MultiBox Detector (SSD) network excels in overall performance, while the anchor-free Fully Convolutional One-stage Object Detector (FCOS) exhibits the best generalization ability. Moreover, the results also indicate that, in the context of breast lesion detection, anchor-based networks generally outperform anchor-free networks.

Keywords:

deep learning breast ultrasound image breast cancer breast lesion detection object detection

References

  1. 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: A Cancer J. Clin. 2018, 68, 394–424. DOI: https://doi.org/10.3322/caac.21492
  2. Sun, Y.S.; Zhao, Z.; Yang, Z.N.; Xu, F.; Lu, H.J.; Zhu, Z.Y.; Shi, W.; Jiang, J.; Yao, P.P.; Zhu, H.P. Risk factors and preventions of breast cancer. Int. J. Biol. Sci. 2017, 13, 1387. DOI: https://doi.org/10.7150/ijbs.21635
  3. Elmore, J.G.; Armstrong, K.; Lehman, C.D.; Fletcher, S.W. Screening for breast cancer. JAMA 2005, 293, 1245–1256. DOI: https://doi.org/10.1001/jama.293.10.1245
  4. Geisel, J.; Raghu, M.; Hooley, R. The Role of Ultrasound in Breast Cancer Screening: The Case for and against Ultrasound; Seminars in Ultrasound, CT and MRI. Elsevier: Amsterdam, The Netherlands, 2018; Volume 39, pp. 25–34. DOI: https://doi.org/10.1053/j.sult.2017.09.006
  5. Qian, X.; Pei, J.; Zheng, H.; Xie, X.; Yan, L.; Zhang, H.; Han, C.; Gao, X.; Zhang, H.; Zheng, W.; et al. Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat. Biomed. Eng. 2021, 5, 522–532. DOI: https://doi.org/10.1038/s41551-021-00711-2
  6. Zhu, Z.; Wang, S.H.; Zhang, Y.D. A Survey of Convolutional Neural Network in Breast Cancer. Comput. Model. Eng. Sci. 2023, 136, 2127–2172. DOI: https://doi.org/10.32604/cmes.2023.025484
  7. Drukker, K.; Giger, M.L.; Horsch, K.; Kupinski, M.A.; Vyborny, C.J.; Mendelson, E.B. Computerized lesion detection on breast ultrasound. Med. Phys. 2002, 29, 1438–1446. DOI: https://doi.org/10.1118/1.1485995
  8. Liu, B.; Cheng, H.; Huang, J.; Tian, J.; Liu, J.; Tang, X. Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance. Ultrasound Med. Biol. 2009, 35, 1309–1324. DOI: https://doi.org/10.1016/j.ultrasmedbio.2008.12.007
  9. Shan, J.; Cheng, H.; Wang, Y. Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med. Biol. 2012, 38, 262–275. DOI: https://doi.org/10.1016/j.ultrasmedbio.2011.10.022
  10. Yap, M.H.; Goyal, M.; Osman, F.; Mart´ı, R.; Denton, E.; Juette, A.; Zwiggelaar, R. Breast ultrasound region of interest detection and lesion localisation. Artif. Intell. Med. 2020, 107, 101880. DOI: https://doi.org/10.1016/j.artmed.2020.101880
  11. Ren, S.; He, K.; Girshick, R.; Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 2016, 39, 1137–1149. DOI: https://doi.org/10.1109/TPAMI.2016.2577031
  12. Wang, Y.; Yao, Y. Breast lesion detection using an anchor-free network from ultrasound images with segmentation-based enhancement. Sci. Rep. 2022, 12, 14720. DOI: https://doi.org/10.1038/s41598-022-18747-y
  13. Tian, Z.; Shen, C.; Chen, H.; He, T. Fcos: Fully convolutional one-stage object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27 October–2 November 2019; pp. 9627–9636. DOI: https://doi.org/10.1109/ICCV.2019.00972
  14. Cao, Z.; Duan, L.; Yang, G.; Yue, T.; Chen, Q. An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med. Imaging 2019, 19, 1–9. DOI: https://doi.org/10.1186/s12880-019-0349-x
  15. Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.Y.; Berg, A.C. Ssd: Single shot multibox detector. In Proceedings of the Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11–14 October 2016; Part I 14, pp. 21–37. DOI: https://doi.org/10.1007/978-3-319-46448-0_2
  16. Mo, W.; Zhu, Y.; Wang, C. A method for localization and classification of breast ultrasound tumors. In Proceedings of the Advances in Swarm Intelligence: 11th International Conference, ICSI 2020, Belgrade, Serbia, 14–20 July 2020; pp. 564–574. DOI: https://doi.org/10.1007/978-3-030-53956-6_52
  17. Redmon, J.; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018, arXiv:1804.02767.
  18. Yu, X.; Zhu, Z.; Alon, Y.; Guttery, D.S.; Zhang, Y. GFNet: A Deep Learning Framework for Breast Mass Detection. Electronics 2023, 12, 1583. DOI: https://doi.org/10.3390/electronics12071583
  19. Lin, T.Y.; Goyal, P.; Girshick, R.; He, K.; Doll´ar, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 2980–2988. DOI: https://doi.org/10.1109/ICCV.2017.324
  20. Chen, Q.; Wang, Y.; Yang, T.; Zhang, X.; Cheng, J.; Sun, J. You only look one-level feature. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 13039–13048. DOI: https://doi.org/10.1109/CVPR46437.2021.01284
  21. Law, H.; Deng, J. Cornernet: Detecting objects as paired keypoints. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 734–750. DOI: https://doi.org/10.1007/978-3-030-01264-9_45
  22. Liu, Z.; Zheng, T.; Xu, G.; Yang, Z.; Liu, H.; Cai, D. Training-time-friendly network for real-time object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, New York, New York, USA, 7–12 February 2020; Volume 34, pp. 11685–11692. DOI: https://doi.org/10.1609/aaai.v34i07.6838
  23. Ge, Z.; Liu, S.; Wang, F.; Li, Z.; Sun, J. Yolox: Exceeding yolo series in 2021. arXiv 2021, arXiv:2107.08430.
  24. Yap, M.H.; Pons, G.; Marti, J.; Ganau, S.; Sentis, M.; Zwiggelaar, R.; Davison, A.K.; Marti, R. Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J. Biomed. Health Inform. 2017, 22, 1218–1226. DOI: https://doi.org/10.1109/JBHI.2017.2731873
  25. Al-Dhabyani, W.; Gomaa, M.; Khaled, H.; Fahmy, A. Dataset of breast ultrasound images. Data Brief 2020, 28, 104863. DOI: https://doi.org/10.1016/j.dib.2019.104863
  26. Girshick, R.; Donahue, J.; Darrell, T.; Malik, J. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; pp. 580–587. DOI: https://doi.org/10.1109/CVPR.2014.81
  27. 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, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778. DOI: https://doi.org/10.1109/CVPR.2016.90
  28. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
  29. Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 779–788. DOI: https://doi.org/10.1109/CVPR.2016.91
  30. PaddlePaddle. PaddleDetection, Object Detection and Instance Segmentation Toolkit Based on PaddlePaddle; PaddlePaddle: Haidian, Beijing, 2019.
  31. Loshchilov, I.; Hutter, F. Sgdr: Stochastic gradient descent with warm restarts. arXiv 2016, arXiv:1608.03983.
  32. Bochkovskiy, A.; Wang, C.Y.; Liao, H.Y.M. Yolov4: Optimal speed and accuracy of object detection. arXiv 2020, arXiv:2004.10934.