Skip to main content

Advertisement

SpringerLink
Log in

Detection and classification of pulmonary nodules using deep learning and swarm intelligence

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Cancer diagnosis is usually an arduous task in medicine, especially when it comes to pulmonary cancer, which is one of the most deadly and hard to treat types of that disease. Early detecting pulmonary cancerous nodules drastically increases surviving chances but also makes it an even harder problem to solve, as it mostly depends on a visual inspection of tomography scans. In order to help improving cancer detection and surviving rates, engineers and scientists have been developing computer-aided diagnosis systems, similar to the one presented in this paper. These systems are used as second opinions, to help health professionals during the diagnosis of numerous diseases. This work uses computational intelligence techniques to propose a new approach towards solving the problem of detecting pulmonary carcinogenic nodules in computed tomography scans. The applied technology consists of using Deep Learning and Swarm Intelligence to develop different nodule detection and classification models. We exploit seven different swarm intelligence algorithms and convolutional neural networks, prepared for biomedical image segmentation, to find and classify cancerous pulmonary nodules in the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) databases. The aim of this work is to use swarm intelligence to train convolutional neural networks and verify whether this approach brings more efficiency than the classic training algorithms, such as back-propagation and gradient descent methods. As main contribution, this work confirms the superiority of swarm-trained models over the back-propagation-based model for this application, as three out of the seven algorithms are proved to be superior regarding all four performance metrics, which are accuracy, precision, sensitivity, and specificity, as well as training time, where the best swarm-trained model operates 25% faster than the back-propagation model. The performed experiments show that the developed models can achieve up to 93.71% accuracy, 93.53% precision, 92.96% sensitivity, and 98.52% specificity.

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

Taken from [6]

Fig. 2

Taken from [6]

Fig. 3
Fig. 4
Fig. 5
Fig. 6

Taken from [4]

Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. American Society of Clinical Oncology (2018) Biopsy. [Online; Accessed 10 Oct 2018]

  2. Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA et al (2011) The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Med Phys 38(2):915–931

    Article  Google Scholar 

  3. Chon A, Balachandra N, Lu P (2017) Deep convolutional neural networks for lung cancer detection. Standford University

  4. Fabian Isensee (2018) U-net. [Online; Accessed 10 Oct 2018]

  5. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  6. Ritchie H, Roser M (2019) Causes of death. [Online; Accessed 10 Oct 2018]

  7. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  8. Jayanthi SK, Preetha K (2016) Breast cancer detection and classification using artificial neural network with particle swarm optimization. Int J Adv Res Basic Eng Sci Technol, 2

  9. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Global Optim 39(3):459–471

    Article  MathSciNet  Google Scholar 

  10. Kennedy J, Eberhart R (1995) Particle swarm optimization

  11. Khajehzadeh M, Eslami M (2012) Gravitational search algorithm for optimization of retaining structures. Indian J Sci Technol, 5(1)

  12. Kuan K, Ravaut M, Manek G, Chen H, Lin J, Nazir B, Chen C, Howe TC, Zeng Z, Chandrasekhar V (2017) Deep learning for lung cancer detection: tackling the kaggle data science bowl 2017 challenge. arXiv:1705.09435

  13. Lee MC, Boroczky L, Sungur-Stasik K, Cann AD, Borczuk AC, Kawut SM, Powell CA (2010) Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction. Artif Intell Med 50(1):43–53

    Article  Google Scholar 

  14. Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD (2008) Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neur Netw 21(2-3):427–436

    Article  Google Scholar 

  15. Mhetre MRR, Sache MRG Detection of lung cancer nodule on ct scan images by using region growing method

  16. Miah MBA, Yousuf MA (2015) Detection of lung cancer from ct image using image processing and neural network. In: 2015 International conference on electrical engineering and information communication technology (ICEEICT), pp 1–6

  17. Morais R, Mourelle LM, Nedjah N (2018) Hitchcock birds inspired algorithm: 10th international conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part ii, pp 169–180, 01

  18. National Institutes of Health (2011) Cancer costs projected to reach at least 158 billion in 2020. [Online; Accessed 10 Oct 2018]

  19. Passino KM (2010) Bacterial foraging optimization. Int J Swarm Intell Res 1(1):1–16

    Article  MathSciNet  Google Scholar 

  20. Ritthipakdee T, Premasathian J (2017) Firefly mating algorithm for continuous optimization problems 2017

  21. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

  22. Sivakumar S, Chandrasekar C (2013) Lung nodule detection using fuzzy clustering and support vector machines. Int J Eng Technol 5(1):179–185

    Google Scholar 

  23. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  24. Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: Proceedings of the first international conference on advances in swarm intelligence - volume part I, ICSI’10. Springer, Berlin, pp 355–364

  25. Wang S, Zhang Y, Dong Z, Du S, Ji G, Yan J, Yang J, Wang Q, Feng C, Phillips P (2015) Feed-forward neural network optimized by hybridization of pso and abc for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164

    Article  Google Scholar 

  26. Zhang Y, Wang S, Wu L (2010) A novel method for magnetic resonance brain image classification based on adaptive chaotic pso. Prog Electromagn Res 109:325–343

    Article  Google Scholar 

  27. Zhang Y, Wang S, Phillips P, Dong Z, Ji G, Yang J (2015) Detection of alzheimer’s disease and mild cognitive impairment based on structural volumetric mr images using 3d-dwt and wta-ksvm trained by psotvac. Biomed Signal Process Control 21:58–73

    Article  Google Scholar 

  28. Zhou Z-H, Jiang Y, Yang Y-B, Chen S-F (2002) Lung cancer cell identification based on artificial neural network ensembles. Artif Intell Med 24(1):25–36

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics Engineering and Telecommunications, Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Cesar Affonso de Pinho Pinheiro & Nadia Nedjah

  2. Department of Systems Engineering and Computation, Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Luiza de Macedo Mourelle

Authors
  1. Cesar Affonso de Pinho Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadia Nedjah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luiza de Macedo Mourelle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadia Nedjah.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Pinho Pinheiro, C.A., Nedjah, N. & de Macedo Mourelle, L. Detection and classification of pulmonary nodules using deep learning and swarm intelligence. Multimed Tools Appl 79, 15437–15465 (2020). https://doi.org/10.1007/s11042-019-7473-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7473-z

Keywords

Search

Navigation