Skip to main content
SpringerLink
Log in

Federated learning: a deep learning model based on resnet18 dual path for lung nodule detection

  • Track 2: Medical Applications of Multimedia
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

A Correction to this article was published on 09 December 2022

This article has been updated

Abstract

Lung nodule detection is of vital importance in the prevention of lung cancer. In the past two decades, most machine learning and deep learning approaches have focused on training models using data collected and stored in centralised data repositories. However, as privacy security becoming more and more important, patient data is scattered in different medical institutions on a small scale and fragmented. In this study, we proposed a federated learning method for training a lung nodule detection model on horizontally distributed data from different clients. In particular, the federated averaging algorithm is used to detect lung nodules by proposing a 3D ResNet18 Dual Path Faster R-CNN model. On this basis, we firstly considered that the quality of the data affects the model training effect. Therefore, we proposed a sampling-based content diversity algorithm that is validated on luna16 data, mitigating model overfitting and improving model generalisation with better results, and also reducing the training time of model. In order to further verify 3D ResNet18 Dual Path Faster R-CNN of federated learning algorithm, we compared it with other federated learning algorithms of deep learning. The experimental results show that the 3D ResNet18 Dual Path Faster R-CNN of federated learning algorithm achieves the best results.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Change history

References

  1. Ambati LS, El-Gayar O (2019) Human activity recognition: a comparison of machine learning approaches. J Midwest Assoc Inf Syst 2021(1):49

    Google Scholar 

  2. Ambati LS, El-Gayar O, Nawar N (2020) Influence of the Digital divide and socio-economic factors on prevalence of diabetes. Issues Inf Syst 21(4):103–113

    Google Scholar 

  3. Ambati LS, El-Gayar O, Nawar N (2021) "Design Principles for Multiple Sclerosis Mobile Self-Management Applications: A Patient-Centric Perspective". AMCIS 2021 Proceedings. 11

  4. Baldwin DR (2015 Jul) Prediction of risk of lung cancer in populations and in pulmonary nodules: significant progress to drive changes in paradigms. Lung Cancer 89(1):1–3. https://doi.org/10.1016/j.lungcan.2015.05.004

  5. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424. https://doi.org/10.3322/caac.21492 Epub 2018 Sep 12. Erratum in: CA Cancer J Clin. 2020 Jul;70(4):313

    Article  Google Scholar 

  6. Chen K, Franko K, Sang R (2021) Structured model pruning of convolutional networks on tensor processing units. https://doi.org/10.48550/arXiv.2107.04191

  7. Chunran Y, Yuanvuan W, Yi G (2018) Automatic detection and segmentation of lung nodule on CT images. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp 1–6. https://doi.org/10.1109/CISP-BMEI.2018.8633101

  8. Cornwell WK, Schwilk LD, Ackerly DD (2006) A trait-based test for habitat filtering: convex hull volume. Ecology 87(6):1465–1471. https://doi.org/10.1890/0012-9658(2006)87[1465:attfhf]2.0.co;2

  9. Ding J, Li A, Hu Z, Wang L (2017) Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins D, Duchesne S (eds) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol 10435. Springer, Cham. https://doi.org/10.1007/978-3-319-66179-7_64

  10. Dou Q, Chen H, Jin Y, Lin H, Qin J, Heng PA (2017) Automated pulmonary nodule detection via 3D ConvNets with online sample filtering and hybrid-loss residual learning. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins D, Duchesne S (eds) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, vol 10435. Springer, Cham. https://doi.org/10.1007/978-3-319-66179-7_72

  11. Dou Q, Chen H, Yu L, Qin J, Heng P (2017) Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng 64(7):1558–1567. https://doi.org/10.1109/TBME.2016.2613502

    Article  Google Scholar 

  12. El-Gayar OF, Ambati LS, Nawar N (2020) Wearables, artificial intelligence, and the future of healthcare. In: AI and Big Data’s Potential for Disruptive Innovation. IGI Global, pp 104–129

  13. Girshick R (2015) Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp 1440–1448. https://doi.org/10.1109/ICCV.2015.169

  14. Girshick R, Donahue J, Darrell T, Malik J (2016) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158. https://doi.org/10.1109/TPAMI.2015.2437384

  15. Gupta O, Raskar R (2018) Distributed learning of deep neural network over multiple agents. J Netw Comput Appl 116:1–8

  16. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770-778. https://doi.org/10.1109/CVPR.2016.90

  17. He K, Zhang X, Ren S, Sun J (2016) "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, https://doi.org/10.1109/CVPR.2016.90

  18. Henschke CI, Yankelevitz DF, Yip R et al (2012) Lung cancers diagnosed at annual CT screening:volume doubling times[J]. Radiology 263(2):578–558

    Article  Google Scholar 

  19. Huang X, Shan J, Vaidya V (2017) "Lung nodule detection in CT using 3D convolutional neural networks," 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 379–383, https://doi.org/10.1109/ISBI.2017.7950542

  20. Huang X, Sun WQ, Tseng TL et al (2019) Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks[J]. Comput Med Imaging Graph 74:25–36

    Article  Google Scholar 

  21. Jacob C, Gopakumar C (2020) "Pulmonary Nodule Detection Techniques in CT Images: New Strategies and Challenges," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1279–1283, https://doi.org/10.1109/ICACCS48705.2020.9074161

  22. Jain P, Raj Shivwanshi R, Nirala N, Gupta S (2020) SumNet Convolution Neural network based Automated pulmonary nodule detection system. In: 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI), pp 1–6. https://doi.org/10.1109/ICATMRI51801.2020.9398414

  23. Jin H, Li Z, Tong R, Lin L (2018) A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection[J]. Med Phys, 45(5)

  24. Konen J, Mcmahan HB, Yu FX et al (2016) Federated learning: strategies for improving communication efficiency[J]

  25. Konečný J, Mcmahan HB, Ramage D, Richtárik P (2016) Federated optimization: distributed machine learning for on-device intelligence. arXiv: Learning. https://doi.org/10.48550/arXiv.1610.02527

  26. Kumar BS, Kumar MV (2020) Detection of Lung Nodules using Convolution Neural Network: a Review, 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 590–594, https://doi.org/10.1109/ICIRCA48905.2020.9183183

  27. Lavanya M, Arivalagan M, Princye PH, Sivasubramanian S, Madhu S (2020) "A Review on Lung Nodule Segmentation Techniques for Nodule Detection," 2020 4th international conference on electronics, Commun Aerospace Technol (ICECA), pp. 1423–1428, https://doi.org/10.1109/ICECA49313.2020.9297387

  28. Liao F, Liang M, Li Z, Hu X, Song S (2019) Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-or network. IEEE Trans Neural Netw Learn Syst 30(11):3484–3495. https://doi.org/10.1109/TNNLS.2019.2892409

  29. Liu W, Liu X, Li H, Li M, Zhao X, Zhu Z (2021) Integrating lung parenchyma segmentation and nodule detection with deep multi-task learning. IEEE J Biomed Health Inf 25(8):3073–3081. https://doi.org/10.1109/JBHI.2021.3053023

    Article  Google Scholar 

  30. Makkithaya MTK, Narendra VG (2022) "A Federated Learning-Based Crop Yield Prediction for Agricultural Production Risk Management," 2022 IEEE Delhi Section Conference (DELCON), pp. 1–7, https://doi.org/10.1109/DELCON54057.2022.9752836

  31. Mcmahan HB, Moore E, Ramage D et al (2016) Communication-efficient learning of deep networks from decentralized data[J]

  32. Mcmahan HB, Moore E, Ramage D et al (2016) Federated Learning of Deep Networks using Model Averaging[J]

  33. Nawreen N, Hany U, Islam T (2021) "Lung Cancer Detection and Classification using CT Scan Image Processing," 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), pp. 1–6, https://doi.org/10.1109/ACMI53878.2021.9528297

  34. Nguyen CC, Tran GS, Nguyen VT, Burie J-C, Nghiem TP (2021) Pulmonary nodule detection based on faster R-CNN with adaptive anchor box. IEEE Access 9:154740–154751. https://doi.org/10.1109/ACCESS.2021.3128942

    Article  Google Scholar 

  35. Nithila EE, Kumar SS (2016) Segmentation of lung nodule in CT data using active contour model and fuzzy C-mean clustering. Alex Eng J 55(3):2583–2588

    Article  Google Scholar 

  36. Prithvika PCS, Anbarasi LJ (2021) "A Review on Identification and Classification of Lung Nodules," 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 903–908, https://doi.org/10.1109/I-SMAC52330.2021.9641051

  37. Ranzato M, Huang FJ, Boureau Y, LeCun Y (2007) "Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition," 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, https://doi.org/10.1109/CVPR.2007.383157

  38. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  39. Sreekumar A, Nair KR, Sudheer S, Ganesh Nayar H, Nair JJ (2020) "Malignant Lung Nodule Detection using Deep Learning," 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 0209–0212, https://doi.org/10.1109/ICCSP48568.2020.9182258

  40. Tanwar VK, Rajput AS, Raman B, Bhargava R (2018) "Privacy preserving image scaling using 2D Bicubic interpolation over the cloud," 2018 IEEE international conference on systems, man, and cybernetics (SMC), pp. 2073-2078, https://doi.org/10.1109/SMC.2018.00357

  41. Ullah MI, Kuri SK (2020) "Lung nodule Detection and Classification using Deep Neural Network," 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1062–1065, https://doi.org/10.1109/TENSYMP50017.2020.9230793

  42. Wang Z et al (2015) Exploring fifisher vector and deep networks for action spotting. In CVPRW

  43. Wang Z et al (2017) Weakly supervised patchnets: describing and aggregating local patches for scene recognition. IEEE TIP

  44. Wang Z et al (2018) Structed triplets learning with pos-tag guided attention for visual question answering. In WACV

  45. Xie D, Tang C, Li Y, Liu X, Zhuang M (2021) Pulmonary nodules detection via 3D multi-scale dual path network. In: 2021 7th International Conference on Computer and Communications (ICCC), pp 980–984. https://doi.org/10.1109/ICCC54389.2021.9674613

  46. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications[J]. ACM Trans Intell Syst Technol (TIST) 10(2):1–19

    Article  Google Scholar 

  47. Zhang W et al (2021) Dynamic-fusion-based federated learning for COVID-19 detection. IEEE Internet Things J 8(21):15884–15891. https://doi.org/10.1109/JIOT.2021.3056185

  48. Zhu W, Xiang X, Tran TDT, Hager GDH, Xie X (2018) Adversarial deep structured nets for mass segmentation from mammograms. IEEE International Symposium on Biomedical Imaging

  49. Zhu W, Liu C, Fan W, Xie X (2018) DeepLung: deep 3D dual path nets for automated pulmonary nodule detection and classification. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp 673-681. https://doi.org/10.1109/WACV.2018.00079

Download references

Funding

This research was funded by National Key Research and Development Program of China (2021ZD0200406), and National Key Science and Technology Program 2030 (No.2021ZD0110601).

Author information

Authors and Affiliations

  1. Guilin University of Electronic Technology, Guilin, China

    Lixin Liu

  2. Chinese Electronics Standardization Institute, Beijing, China

    Kefeng Fan

  3. Shanghai Institute of Technology, Shanghai, China

    Mengzhen Yang

Authors
  1. Lixin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kefeng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengzhen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lixin Liu.

Ethics declarations

Conflict of interest

The authors have no conflict of interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

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

The original online version of this article was revised due to a retrospective Open Access cancellation.

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

Liu, L., Fan, K. & Yang, M. Federated learning: a deep learning model based on resnet18 dual path for lung nodule detection. Multimed Tools Appl 82, 17437–17450 (2023). https://doi.org/10.1007/s11042-022-14107-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14107-0

Keywords

Search

Navigation