Advertisement

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

  • Benita K. J. VeronicaEmail author
Article
  • 5 Downloads

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.

Keywords

LN detection ROI extraction Segmentation FCM Feature extraction Classification ANN 

Notes

References

  1. 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):351–359Google Scholar
  2. 2.
    Badura P, Pietka E (2014) Soft computing approach to 3D lung nodule segmentation in CT. Comput Biol Med 53:230–243CrossRefGoogle Scholar
  3. 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):18929–18954CrossRefGoogle Scholar
  4. 4.
    Bhuvaneswari P, Therese AB (2015) Detection of cancer in lung with k-nn classification using genetic algorithm. Procedia Mater Sci 10:433–440CrossRefGoogle Scholar
  5. 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):327–350MathSciNetCrossRefGoogle Scholar
  6. 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 ApplGoogle Scholar
  7. 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):19039–19055CrossRefGoogle Scholar
  8. 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 162:109–118CrossRefGoogle Scholar
  9. 9.
  10. 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 ApplGoogle Scholar
  11. 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):69–75CrossRefGoogle Scholar
  12. 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 165:215–224CrossRefGoogle Scholar
  13. 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 69:176–188CrossRefGoogle Scholar
  14. 14.
    Gonçalves L, Novo J, Campilho A (2016) Hessian based approaches for 3D lung nodule segmentation. Expert Syst Appl 61:1–5CrossRefGoogle Scholar
  15. 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 135:125–139CrossRefGoogle Scholar
  16. 16.
    John J, Mini MG (2016) Multilevelthresholding based segmentation and feature extraction for pulmonary nodule detection. Procedia Technology 24:957–963CrossRefGoogle Scholar
  17. 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):287–300CrossRefGoogle Scholar
  18. 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 ApplGoogle Scholar
  19. 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 77:262–275CrossRefGoogle Scholar
  20. 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 77:262–275CrossRefGoogle Scholar
  21. 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):347–360CrossRefGoogle Scholar
  22. 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 ApplGoogle Scholar
  23. 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):1192–1202CrossRefGoogle Scholar
  24. 24.
    Shakir H, Khan TM, Rasheed H (2018) 3-D segmentation of lung nodules using hybrid level sets. Comput Biol Med 96:214–226CrossRefGoogle Scholar
  25. 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 57:139–149CrossRefGoogle Scholar
  26. 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 ApplGoogle Scholar
  27. 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 162:109–118CrossRefGoogle Scholar
  28. 28.
    Skourt BA, El Hassani A, Majda A (2018) Lung CT image segmentation using deep neural networks. Procedia Computer Science 127:109–113CrossRefGoogle Scholar
  29. 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 100:108–115CrossRefGoogle Scholar
  30. 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):140–151CrossRefGoogle Scholar
  31. 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 161:173–180CrossRefGoogle Scholar
  32. 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 19:2Google Scholar
  33. 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 ApplGoogle Scholar
  34. 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 85:109–119CrossRefGoogle Scholar
  35. 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 42:102–110CrossRefGoogle Scholar
  36. 36.
    Yuan J, Liu X, Hou F, Qin H, Hao A (2018) Hybrid-feature-guided lung nodule type classification on CT images. Comput Graph 70:288–299CrossRefGoogle Scholar
  37. 37.
    Zawbaa HM, Emary E, Parv B (2015) Feature selection based on antlion optimization algorithm. In2015 third world conference on complex systems (WCCS) 1-7Google Scholar
  38. 38.
    Zhang J, Xia Y, Cui H, Zhang Y (2018) Pulmonary nodule detection in medical images: a survey. Biomedical Signal Processing and Control 43:138–147CrossRefGoogle Scholar
  39. 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 IntGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Mother Teresa UniversityTamil NaduIndia

Personalised recommendations