[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

An effective neural network model for lung nodule detection in CT images with optimal fuzzy model

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

Abstract

Cancer disease is assumed as a gathering of diseases which is initiated because of uncontrolled cell growth. An early analysis of Lung Nodules (LN) can possibly enhance the prognosis and in future can save numerous lives every year. In the proposed research work, the LN detection from the ELCAP lung image database is analyzed by image segmentation and classification techniques. Initially, the exact portion of the lung image is achieved and then it is subjected to pre-processing where the image contrast level is enhanced by the imadjust function of MATLAB. Next to that, the potential nodules are segmented by the Fuzzy C-Means (FCM) and then some features are extracted for effective classification. Based on the selected or extracted feature sets, the images are classified as two types (nodule detected and normal lung) by the proposed classifier i.e. Artificial Neural Network (ANN) with weight optimization. The performances of the proposed algorithm and classifier are tested on the chosen datasets in terms of sensitivity, specificity and accuracy. The results demonstrate that ANN with Oppositional based Ant Lion Optimization (OALO) algorithm achieves high accuracy and less execution time compared to existing algorithms.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /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 includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Asuntha A, Singh N, Srinivasan A (2016) PSO, genetic optimization and SVM algorithm used for lung cancer detection. J Chem Pharm Res 8(6):351–359

    Google Scholar 

  2. Badura P, Pietka E (2014) Soft computing approach to 3D lung nodule segmentation in CT. Comput Biol Med 53:230–243

    Article  Google Scholar 

  3. Barros Netto SM, Silva AC, Cardoso de Paiva A, Nunes RA, Gattass M (2017) Unsupervised detection of density changes through principal component analysis for lung lesion classification. Multimed Tools Appl 76(18):18929–18954

    Article  Google Scholar 

  4. Bhuvaneswari P, Therese AB (2015) Detection of cancer in lung with k-nn classification using genetic algorithm. Procedia Mater Sci 10:433–440

    Article  Google Scholar 

  5. Bong CW, Lam HY, Khader AT, Kamarulzaman H (2012) Adaptive multi-objective archive-based hybrid scatter search for segmentation in lung computed tomography imaging. Eng Optim 44(3):327–350

    Article  MathSciNet  Google Scholar 

  6. Cao M, Wang S, Wei L, Rai L, Li D, Yu H, Shao D (2018) Segmentation of immunohistochemical image of lung neuroendocrine tumor based on double layer watershed. Multimed Tools Appl

  7. Da Silva GLF, da Silva Neto OP, Silva AC, de Paiva AC, Gattass M (2017) Lung nodules diagnosis based on evolutionary convolutional neural network. Multimed Tools Appl 76(18):19039–19055

    Article  Google Scholar 

  8. da Silva GL, Valente TL, Silva AC, de Paiva AC, Gattass M (2018) Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. Comput Methods Prog Biomed 162:109–118

    Article  Google Scholar 

  9. Database: http://www.via.cornell.edu/lungdb.html

  10. De Pinho Pinheiro CA, Nedjah N, de Macedo Mourelle L (2019) Detection and classification of pulmonary nodules using deep learning and swarm intelligence. Multimed Tools Appl

  11. Dwivedi MS, Borse MR, Yametkar MA (2014) Lung cancer detection and classification by using machine learning & multinomial Bayesian. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) 9(1):69–75

    Article  Google Scholar 

  12. Eun H, Kim D, Jung C, Kim C (2018) Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection. Comput Methods Prog Biomed 165:215–224

    Article  Google Scholar 

  13. Froz BR, de CarvalhoFilho AO, Silva AC, de Paiva AC, Nunes RA, Gattass M (2017) Lung nodule classification using artificial crawlers, directional texture and support vector machine. Expert Syst Appl 69:176–188

    Article  Google Scholar 

  14. Gonçalves L, Novo J, Campilho A (2016) Hessian based approaches for 3D lung nodule segmentation. Expert Syst Appl 61:1–5

    Article  Google Scholar 

  15. Javaid M, Javid M, Rehman MZ, Shah SI (2016) A novel approach to CAD system for the detection of lung nodules in CT images. Comput Methods Prog Biomed 135:125–139

    Article  Google Scholar 

  16. John J, Mini MG (2016) Multilevelthresholding based segmentation and feature extraction for pulmonary nodule detection. Procedia Technology 24:957–963

    Article  Google Scholar 

  17. Keshani M, Azimifar Z, Tajeripour F, Boostani R (2013) Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system. Comput Biol Med 43(4):287–300

    Article  Google Scholar 

  18. Li J, Fong S, Liu L, Dey N, Ashour AS, Moraru L (2019) Dual feature selection and rebalancing strategy using metaheuristic optimization algorithms in X-ray image datasets. Multimed Tools Appl

  19. Liu X, Hou F, Qin H, Hao A (2018) Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recogn 77:262–275

    Article  Google Scholar 

  20. Liu X, Hou F, Qin H, Hao A (2018) Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recogn 77:262–275

    Article  Google Scholar 

  21. Majhi SK, Biswal S (2018) Optimal cluster analysis using hybrid K-means and ant lion optimizer. Karbala International Journal of Modern Science 4(4):347–360

    Article  Google Scholar 

  22. Naqi SM, Sharif M, Lali IU (2019) A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection. Multimed Tools Appl

  23. Nithila EE, Kumar SS (2017) Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images. Engineering Science and Technology, an International Journal 20(3):1192–1202

    Article  Google Scholar 

  24. Shakir H, Khan TM, Rasheed H (2018) 3-D segmentation of lung nodules using hybrid level sets. Comput Biol Med 96:214–226

    Article  Google Scholar 

  25. Shen S, Bui AA, Cong J, Hsu W (2015) An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput Biol Med 57:139–149

    Article  Google Scholar 

  26. Shi Z, Hao H, Zhao M, Feng Y, He L, Wang Y, Suzuki K (2018) A deep CNN based transfer learning method for false positive reduction. Multimed Tools Appl

  27. Silva D, Giovanni LF, Thales Levi AV, AristófanesCS ACP, Marcelo G (2018) Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. Comput Methods Prog Biomed 162:109–118

    Article  Google Scholar 

  28. Skourt BA, El Hassani A, Majda A (2018) Lung CT image segmentation using deep neural networks. Procedia Computer Science 127:109–113

    Article  Google Scholar 

  29. Tsubakimoto M, Yamashiro T, Tamashiro Y, Murayama S (2018) Quantitative CT density histogram values and standardized uptake values of FDG-PET/CT with respiratory gating can distinguish solid adenocarcinomas from squamous cell carcinomas of the lung. Eur J Radiol 100:108–115

    Article  Google Scholar 

  30. Ur Rehman MZ, Javaid M, Shah SI, Gilani SO, Jamil M, Butt SI (2018) An appraisal of nodules detection techniques for lung cancer in CT images. Biomedical Signal Processing and Control 1(41):140–151

    Article  Google Scholar 

  31. Woźniak M, Połap D, Capizzi G, Sciuto GL, Kośmider L, Frankiewicz K (2018) Small lung nodules detection based on local variance analysis and probabilistic neural network. Comput Methods Prog Biomed 161:173–180

    Article  Google Scholar 

  32. Wu J, Qian T (2019) A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques. Journal of Medical Artificial Intelligence 19:2

    Google Scholar 

  33. Xiao X, Qiang Z, Zhao J, Qiang Y, Wang P, Han P (2019) A feature extraction method for lung nodules based on a multichannel principal component analysis network (PCANet). Multimed Tools Appl

  34. Xie H, Yang D, Sun N, Chen Z, Zhang Y (2019) Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recogn 85:109–119

    Article  Google Scholar 

  35. Xie Y, Zhang J, Xia Y, Fulham M, Zhang Y (2018) Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Information Fusion 42:102–110

    Article  Google Scholar 

  36. Yuan J, Liu X, Hou F, Qin H, Hao A (2018) Hybrid-feature-guided lung nodule type classification on CT images. Comput Graph 70:288–299

    Article  Google Scholar 

  37. Zawbaa HM, Emary E, Parv B (2015) Feature selection based on antlion optimization algorithm. In2015 third world conference on complex systems (WCCS) 1-7

  38. Zhang J, Xia Y, Cui H, Zhang Y (2018) Pulmonary nodule detection in medical images: a survey. Biomedical Signal Processing and Control 43:138–147

    Article  Google Scholar 

  39. Zhou T, Lu H, Zhang J, Shi H (2016) Pulmonary nodule detection model based on SVM and CT image feature-level fusion with rough sets. Biomed Res Int

Download references

Author information

Authors and Affiliations

  1. Mother Teresa University, Kodaikanal, Tamil Nadu, India

    Benita K. J. Veronica

Authors
  1. Benita K. J. Veronica
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benita K. J. Veronica.

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

Veronica, B.K.J. An effective neural network model for lung nodule detection in CT images with optimal fuzzy model. Multimed Tools Appl 79, 14291–14311 (2020). https://doi.org/10.1007/s11042-020-08618-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08618-x

Keywords

Search

Navigation