Skip to main content

Advertisement

Log in

The digital eye for mammography: deep transfer learning and model ensemble based open-source toolkit for mass detection and classification

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Breast cancer stands as a prevalent malignancy affecting women globally, and a screening method, mammography, boasts reliability for early diagnosis. Nevertheless, interpretive errors during population screening may result in false negatives and positives. To address this, Computer-Aided Detection systems rooted in deep learning have emerged, aiming to reduce both false positive and negative predictions. This study introduces an open-source toolkit called The Digital Eye for Mammography (DEM) and addressing limitations in mammography screening for mass detection and classification. The DEM comprises 11 state-of-the-art object detection architectures and uses a meticulously labeled dataset. It serves as a transfer learning source, and provides ensemble of models from diverse deep-learning architectures, resulting in a more robust solution. Experiments conducted on widely-used datasets indicate that the DEM outperforms existing transfer learning sources by significant margins in terms of true positive rate (TPR). According to the experimental results, the DEM serves as a better transfer learning source for mass detection in pathology-proven InBreast and CBIS-DDSM datasets, presenting improvements 12% and 5% in TPR performance at 0.1 false positive per image (FPPI), respectively. Compared to literature, the DEM achieves lower FPPI values while maintaining higher sensitivity, indicating its potential usage as a transfer learning source. By employing ensemble strategies, the DEM produces more reliable outcomes in our KETEM dataset, reducing FPPI by 49% for BI-RADS 1-2 (Breast Imaging Reporting and Data System) and 46% for BI-RADS 4-5 compared to the best individual model while preserving TPR values. The DEM’s results suggest its ability to attain better performance without requiring complex model hyperparameters optimization. The GitHub repository of the DEM project is publicly available on: https://github.com/ddobvyz/digitaleye-mammography.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. World Health Organization. Breast cancer. Accessed March 21, 2023 (2021)

  2. Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F.: Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021)

    Article  Google Scholar 

  3. Katzen, J., Dodelzon, K.: A review of computer aided detection in mammography. Clin. Imaging 52, 305–309 (2018)

    Article  MATH  Google Scholar 

  4. Sadoughi, F., Kazemy, Z., Hamedan, F., Owji, L., Rahmanikatigari, M., Azadboni, T.T.: Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review. Breast Cancer: Targets and Therapy, pp. 219–230, (2018)

  5. Al-antari, M.A., Muhammed, S., Khaleel, H.A., Idri, A., Ali, S.S.: Deep learning classification models for breast cancer diagnosis in mammography: a survey. J. Healthc. Eng. 2019, 1–14 (2019)

    Google Scholar 

  6. Lee, C.-T., Tsai, M.-H., Lin, Y.-T., Wu, C.-W., Chen, B.-Y., Hsieh, Y.-H., Chen, P.-H.: Breast cancer detection and diagnosis using mammographic data: systematic review. J. Med. Internet Res. 21(7), e14464 (2021)

    MATH  Google Scholar 

  7. Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Sci. Rep. 9(1), 12495 (2019)

    Article  Google Scholar 

  8. Lakshmanan, R., Shiji, T.P., Thomas, V., Jacob, S.M., Pratab, T.: A preprocessing method for reducing search area for architectural distortion in mammographic images. In: 2014 Fourth International Conference on Advances in Computing and Communications, pp. 101–104. IEEE, (2014)

  9. Akselrod-Ballin, A., Chorev, M., Shoshan, Y., Spiro, A., Hazan, A., Melamed, R., Barkan, E., Herzel, E., Naor, S., Karavani, E., et al.: Predicting breast cancer by applying deep learning to linked health records and mammograms. Radiology 292(2), 331–342 (2019)

    Article  Google Scholar 

  10. Salama, W.M., Aly, M.H.: Deep learning in mammography images segmentation and classification: automated cnn approach. Alex. Eng. J. 60(5), 4701–4709 (2021)

    Article  MATH  Google Scholar 

  11. Valvano, G., Santini, G., Martini, N., Ripoli, A., Iacconi, C., Chiappino, D., Della Latta, D.: Convolutional neural networks for the segmentation of microcalcification in mammography imaging. J. Healthc. Eng. 2019(1), 9360941 (2019)

    Google Scholar 

  12. Li, H., Chen, D., Nailon, W.H., Davies, M.E., Laurenson, D.I.: Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography. IEEE Trans. Med. Imaging 41(1), 3–13 (2021)

    Article  Google Scholar 

  13. Akselrod-Ballin, A., Karlinsky, L., Hazan, A., Bakalo, R., Horesh, A.B., Shoshan, Y., Barkan, E.: Deep learning for automatic detection of abnormal findings in breast mammography. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, pp. 321–329. Springer, (2017)

  14. Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with deep learning. Sci. Rep. 8(1), 4165 (2018)

    Article  Google Scholar 

  15. Cao, Z., Yang, Z., Liu, X., Zhang, Y., Wu, S., Lin, R.-S., Huang, L., Han, M., Ma, J.: Deep learning based lesion detection for mammograms. In: 2019 IEEE International Conference on Healthcare Informatics (ICHI), pp. 1–3. IEEE, (2019)

  16. Zhang, Z., Wang, Y., Zhang, J., Mu, X.: Comparison of multiple feature extractors on faster rcnn for breast tumor detection. In 2019 8th International Symposium on Next Generation Electronics (ISNE), pp. 1–4. IEEE, (2019)

  17. Djebbar, K., Mimi, M., Berradja, K., Taleb-Ahmed, A.: Deep convolutional neural networks for detection and classification of tumors in mammograms. In: 2019 6th International Conference on Image and Signal Processing and Their Applications (ISPA), pp. 1–7. IEEE, (2019)

  18. Min, H., Wilson, D., Huang, Y., Liu, S., Crozier, S., Bradley, A.P., Chandra, S.S.: Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and mask r-cnn. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1111–1115. IEEE, (2020)

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

    Article  Google Scholar 

  20. Sun, L., Sun, H., Wang, J., Shuai, W., Zhao, Y., Yong, X.: Breast mass detection in mammography based on image template matching and cnn. Sensors 21(8), 2855 (2021)

    Article  MATH  Google Scholar 

  21. Cao, H., Shiliang, P., Tan, W., Tong, J.: Breast mass detection in digital mammography based on anchor-free architecture. Comput. Methods Progr. Biomed. 205, 106033 (2021)

    Article  Google Scholar 

  22. Kuo, S., Honda, O.: Mammographic mass detection based on data separated ensemble convolution neural network. In: 2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 432–437. IEEE, (2021)

  23. Nagalakshmi, T.: Breast cancer semantic segmentation for accurate breast cancer detection with an ensemble deep neural network. Neural Process. Lett. 54(6), 5185–5198 (2022)

    Article  MATH  Google Scholar 

  24. Ibrokhimov, B., Kang, J.-Y.: Two-stage deep learning method for breast cancer detection using high-resolution mammogram images. Appl. Sci. 12(9), 4616 (2022)

    Article  MATH  Google Scholar 

  25. Jung, H., Kim, B., Lee, I., Yoo, M., Lee, J., Ham, S., Woo, O., Kang, J.: Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS ONE 13(9), e0203355 (2018)

    Article  MATH  Google Scholar 

  26. Wimmer, M., Sluiter, G., Major, D., Lenis, D., Berg, A., Neubauer, T., Bühler, K.: Multi-task fusion for improving mammography screening data classification. IEEE Trans. Med. Imaging 41(4), 937–950 (2021)

    Article  Google Scholar 

  27. Mahoro, E., Akhloufi, M.A.: Breast masses detection on mammograms using recent one-shot deep object detectors. In: 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART), pp. 1–4. IEEE, (2023)

  28. Betancourt, A.S., Tarifa, C.M., Molinara, M., Tortorella, F., Bria, A.: Transformer-based mass detection in digital mammograms. J. Ambient. Intell. Humaniz. Comput. 14(3), 2723–2737 (2023)

    Article  Google Scholar 

  29. Anas, M., Ul Haq, I., Husnain, G., Faraz Jaffery, S.A.: Advancing breast cancer detection: Enhancing yolov5 network for accurate classification in mammogram images. IEEE Access, (2024)

  30. Prinzi, F., Insalaco, M., Orlando, A., Gaglio, S., Vitabile, S.: A yolo-based model for breast cancer detection in mammograms. Cogn. Comput. 16(1), 107–120 (2024)

    Article  Google Scholar 

  31. Frank, S.J.: A deep learning architecture with an object-detection algorithm and a convolutional neural network for breast mass detection and visualization. Healthc. Anal. 3, 100186 (2023)

    Article  Google Scholar 

  32. Kebede, S.R., Waldamichael, F.G., Debelee, T.G., Aleme, M., Bedane, W., Mezgebu, B., Merga, Z.C.: Dual view deep learning for enhanced breast cancer screening using mammography. Sci. Rep. 14(1), 3839 (2024)

    Article  Google Scholar 

  33. Chen, Z., Zhao, Z., Abba, A.A.: Detection of microcalcifications in mammograms based on hyper faster r-cnn. In: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence, pp. 1–8, (2021)

  34. Devika, R., Rajasekaran, S., Gayathri, R.L., Priyal, J., Kanneganti, S.R.: Automatic breast cancer lesion detection and classification in mammograms using faster r-cnn deep learning network. Issues Dev. Med. Med. Res. 6, 10–20 (2022)

    Google Scholar 

  35. Kolchev, A., Pasynkov, D., Egoshin, I., Kliouchkin, I., Pasynkova, O., Tumakov, D.: Yolov4-based cnn model versus nested contours algorithm in the suspicious lesion detection on the mammography image: A direct comparison in the real clinical settings. J. Imaging 8(4), 88 (2022)

    Article  Google Scholar 

  36. Meena, G., Mohbey, K.K., Kumar, S.: Monkeypox recognition and prediction from visuals using deep transfer learning-based neural networks. Multimed. Tools Appl. 1–25 (2024)

  37. 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  MATH  Google Scholar 

  38. 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 

  39. Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., Zhang, Z., Cheng, D., Zhu, C., Cheng, T., Zhao, Q., Li, B., Lu, X., Zhu, R., Wu, Y., Dai, J., Wang, J., Shi, J., Ouyang, W., Loy, C.C., Lin, D.: MMDetection: Open mmlab detection toolbox and benchmark. (2019) arXiv preprint arXiv:1906.07155,

  40. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, (2015)

  41. Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., Fu, Y.: Rethinking classification and localization for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10186–10195, (2020)

  42. Zhang, H., Chang, H., Ma, B., Wang, N., Chen, X.: Dynamic r-cnn: Towards high quality object detection via dynamic training. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16, pp. 260–275. Springer, (2020)

  43. Cai, Z., Vasconcelos, N.: Cascade r-cnn: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483–1498 (2019)

    Article  MATH  Google Scholar 

  44. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018) arXiv preprint arXiv:1804.02767

  45. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988, (2017)

  46. 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, pp. 9627–9636, (2019)

  47. Zhang, H., Wang, Y., Dayoub, F., Sunderhauf, N. Varifocalnet: an iou-aware dense object detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8514–8523, (2021)

  48. Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9759–9768, (2020)

  49. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pp. 213–229. Springer, (2020)

  50. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection (2020) arXiv preprint arXiv:2010.04159

  51. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  MATH  Google Scholar 

  52. Solovyev, R., Wang, W., Gabruseva, T.: Weighted boxes fusion: Ensembling boxes from different object detection models. Image Vis. Comput. 107, 104117 (2021)

    Article  Google Scholar 

  53. Hosang, J., Benenson, R., Schiele, B.: Learning non-maximum suppression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4507–4515 (2017)

  54. Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-nms–improving object detection with one line of code. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5561–5569 (2017)

  55. Padilla, R., Netto, S.L., Da Silva, E.A.B.: A survey on performance metrics for object-detection algorithms. In: 2020 International Conference on Systems, Signals and Image Processing, pp. 237–242. IEEE, (2020)

  56. Yongye, S., Liu, Q., Xie, W., Pingzhao, H.: Yolo-logo: a transformer-based yolo segmentation model for breast mass detection and segmentation in digital mammograms. Comput. Methods Progr. Biomed. 221, 106903 (2022)

    Article  Google Scholar 

  57. Mohiyuddin, A., Basharat, A., Ghani, U., Peter, V., Abbas, S., Naeem, O.B., Rizwan, M.: Breast tumor detection and classification in mammogram images using modified yolov5 network. Comput. Math. Methods Med. 1–16, 2022 (2022)

    Google Scholar 

  58. Peng, J., Bao, C., Chuting, H., Wang, X., Jian, W., Liu, W.: Automated mammographic mass detection using deformable convolution and multiscale features. Med. Biol. Eng. Comput. 58, 1405–1417 (2020)

    Article  MATH  Google Scholar 

  59. Jiang, J., Peng, J., Chuting, H., Jian, W., Wang, X., Liu, W.: Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on paa algorithm. Artif. Intell. Med. 134, 102419 (2022)

    Article  Google Scholar 

  60. Dhungel, N., Carneiro, G., Bradley, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal. 37, 114–128 (2017)

    Article  MATH  Google Scholar 

  61. Dhungel, N., Carneiro, G., Bradley, A.P.: The automated learning of deep features for breast mass classification from mammograms. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19, pp. 106–114. Springer, (2016)

  62. Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE, (2015)

  63. Kozegar, E., Soryani, M., Minaei, B., Domingues, I.: Assessment of a novel mass detection algorithm in mammograms. J. Cancer Res. Ther. 9(4), 592–600 (2013)

    Article  Google Scholar 

  64. 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), 031409–031409 (2019)

    Article  Google Scholar 

  65. Zhang, L., Li, Y., Chen, H., Wen, W., Chen, K., Wang, S.: Anchor-free yolov3 for mass detection in mammogram. Expert Syst. Appl. 191, 116273 (2022)

    Article  MATH  Google Scholar 

  66. Al-Antari, M.A., Al-Masni, M.A., Choi, M.-T., Han, S.-M., Kim, T.-S.: A fully integrated computer-aided diagnosis system for digital x-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inf. 117, 44–54 (2018)

    Article  Google Scholar 

  67. Li, Y., Zhang, L., Chen, H., Cheng, L.: Mass detection in mammograms by bilateral analysis using convolution neural network. Comput. Methods Programs Biomed. 195, 105518 (2020)

    Article  Google Scholar 

  68. Gao, F., Yoon, H., Teresa, W., Chu, X.: A feature transfer enabled multi-task deep learning model on medical imaging. Expert Syst. Appl. 143, 112957 (2020)

    Article  MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thanks Republic of Türkiye Ministry of Health for their valuable support.

Author information

Authors and Affiliations

Authors

Contributions

R.T devised the project, the main conceptual ideas and proof outline. A.E.K and G.K. implement the models and performed the experimental studies. O.B.Ö drafted the manuscript and was involved in planning. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Ramazan Terzi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Terzi, R., Kılıç, A.E., Karaahmetoğlu, G. et al. The digital eye for mammography: deep transfer learning and model ensemble based open-source toolkit for mass detection and classification. SIViP 19, 170 (2025). https://doi.org/10.1007/s11760-024-03737-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03737-6

Keywords

Navigation