Skip to main content
SpringerLink
Log in

An Automated CAD System for Classification of Lung Module

  • Conference paper
  • First Online:
Cognition and Recognition (ICCR 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1697))

Included in the following conference series:

Abstract

The main objective of the proposed work is to develop an automated CAD system for classification of lung nodules using various classifiers from CT images. The classification of nodule and non-nodule patterns in CT is one of the most significant processes during the detection of lung nodule. This helps in detecting the disease at early stage thereby decrease the mortality rate. The developed CAD systems consist of segmentation, feature extraction and classification. For Segmentation we used Fuzzy C Means (FCM) for effective extraction infected region. Later, we extracted features through First order statistics (FOS) and Second order statistics (SOS) and fed into classifiers like DS, RF and BPNN. The experimentation is conducted on LIDC-IDRI dataset and results outperforms well with BPNN when compare to other classifiers.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Akbari, R., Ziarati, K.: A rank based particle swarm optimization algorithm with dynamic adaptation. J. Comput. Appl. Math. 235(8), 2694–2714 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  2. Aoyama, M., Li, Q., Katsuragawa, S., Li, F., Sone, S.: Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low- dose ct images. Med. Phys. 30(3), 387–394 (2003)

    Google Scholar 

  3. Armato, S.G., Giger, M.L., Moran, C.J., Blackburn, J.T., Doi, K., MacMahon, H.: Computerized detection of pulmonary nodules on ct scans. Radiographics 19(5), 1303–1311 (1999)

    Article  Google Scholar 

  4. Armato, S.G., et al.: Guest editorial: Lungx chal- lenge for computerized lung nodule classification: Reflections and lessons learned. J. Med. Imag. 2(2), 1–5 (2015)

    Article  Google Scholar 

  5. Armato, S.G., et al.: 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 (2011)

    Article  Google Scholar 

  6. Arumugam, M.S., Rao, M.: On the performance of the particle swarm optimiza- tion algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems. In: Discrete Dynamics in Nature and Society (2006)

    Google Scholar 

  7. Choi, W.-J., Choi, T.-S.: Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput. Methods Prog. Biomed. 113(1), 37–54 (2014)

    Article  Google Scholar 

  8. Criminisi, A., Shotton, J., Bucciarelli, S.: Decision forests with long-range spatial context for organ localization in CT volumes. In: MICCAI Workshop on Probabilistic Models for Medical Image Analysis, Vol. 1 (2009)

    Google Scholar 

  9. Cross, G.R., Jain, A.K.: Markov random field texmre models. IEEE Trans. Pattern Anal. Mach. Intell. 1, 25–39 (1983)

    Article  Google Scholar 

  10. Da Silva Sousa, J.R.F., Silva, A.C., de Paiva, A.C., Nunes, R.A.: Methodology for automatic detection of lung nodules in computerized tomography images. Comput. Methods Prog. Biomed. 98(1), 1–14 (2010)

    Article  Google Scholar 

  11. Dai, S., Lu, K., Dong, J., Zhang, Y., Chen, Y.: A novel approach of lung segmen- tation on chest ct images using graph cuts. Neurocomputing 168, 799–807 (2015)

    Article  Google Scholar 

  12. Daneshmand, F., Mehrshad, N., Massinaei, M.: A new approach for froth image seg- mentation using fuzzy logic. In First Iranian Conference on Pattern Recognition and Image Analysis (PRIA). IEEE (2013)

    Google Scholar 

  13. Dawoud, A.: Lung segmentation in chest radiographs by fusing shape information in iterative thresholding. IET Comput. Vision 5(3), 185–190 (2011)

    Article  Google Scholar 

  14. Sluimer, I., Prokop, M., van Ginneken, B.: Toward automated segmentation of the pathological lung in CT. IEEE Trans. Med. Imaging 24(8), 1025–1038 (2005)

    Article  Google Scholar 

  15. Deep, G., Kaur, L., Gupta, S.: Lung nodule segmentation in ct images using ro- tation invariant local binary pattern. Int. J. Sig. Image Process. 4(1), 20 (2013)

    Google Scholar 

  16. Dehmeshki, J., Amin, H., Valdivieso, M., Ye, X.: Segmentation of pulmonary nodules in thoracic ct scans: A region growing approach. IEEE Trans. Med. Imag. 27(4), 467–480 (2008)

    Article  Google Scholar 

  17. Dehmeshki, J., Ye, X., Lin, X., Valdivieso, M., Amin, H.: Automated detection of lung nodules in ct images using shape-based genetic algorithm. Comput. Med. Imag. Graph. 31(6), 408–417 (2007)

    Article  Google Scholar 

  18. Delogu, P., Cheran, S., De Mitri, I., De Nunzio, G., Fantacci, M., Fauci, F., Gargano, G., Torres, E. L., Massafra, R., Oliva, P., et al.: Preprocessing methods for nodule detection in lung ct. In International Congress Series, Vol. 1281. Elsevier, Amsterdam (2005)

    Google Scholar 

  19. Dheepak, G., Premkumar, S., Ramachandran, R.: Lung cancer detection by using artificial neural network and fuzzy clustering method (2015)

    Google Scholar 

  20. Doi, K.: Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4), 198–211 (2007)

    Article  Google Scholar 

  21. Dolejsi, M., Kybic, J., Polovincak, M., Tuma, S.: The lung time: Annotated lung nodule dataset and nodule detection framework. In: SPIE Medical Imaging. International Society for Optics and Photonics (2009)

    Google Scholar 

  22. Elizabeth, D., Nehemiah, H., Raj, C.R., Kannan, A.: Computer-aided diagno- sis of lung cancer based on analysis of the significant slice of chest computed tomography image. IET Image Process. 6(6), 697–705 (2012)

    Article  Google Scholar 

  23. Enquobahrie, A.A., Reeves, A.P., Yankelevitz, D.F., Henschke, C.I.: Auto- mated detection of small pulmonary nodules in whole lung ct scans. Acad. Radiol. 14(5), 579–593 (2007)

    Article  Google Scholar 

  24. Balakrishna, K., Rao, M.: Tomato plant leaves disease classification using KNN and PNN. Int. J. Comput. Vis. Image Process. 9(1), 51–63 (2019). https://doi.org/10.4018/IJCVIP.2019010104

    Article  Google Scholar 

  25. Farag, A., Ali, A., Graham, J., Farag, A., Elshazly, S., Falk, R.: Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose ct scans of the chest. In IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE (2011)

    Google Scholar 

  26. Farag, A. A., Abdelmunim, H., Graham, J., Farag, A. A., Elshazly, S., El-Mogy, S., El-Mogy, M., Falk, R., Al-Jafary, S., Mahdi, H. et al.: Variational approach for segmentation of lung nodules. In IEEE International Conference on Image Processing (ICIP). IEEE (2011b)

    Google Scholar 

  27. Gambhir, S., et al.: Analytical decision model for the cost-effective management of solitary pulmonary nodules. J. Clin. Oncol. 16(6), 2113–2125 (1998)

    Article  Google Scholar 

  28. Garro, B. A. and R. A. Vazquez (2015). Designing artificial neural networks using particle swarm optimization algorithms. Comput. Intell. Neurosci. 61 (2015)

    Google Scholar 

  29. Golosio, B., et al.: A novel multithreshold method for nodule detection in lung ct. Med. Phys. 36(8), 3607–3618 (2009)

    Article  Google Scholar 

  30. Gomathi, M., Thangaraj, P.: A computer aided diagnosis system for detection of lung cancer nodules using extreme learning machine. Int. J. Eng. Sci. Technol. 2(10), 5770–5779 (2010)

    Google Scholar 

  31. Gomathi, M., Thangaraj, P.: A computer aided diagnosis system for lung cancer detection using support vector machine. Am. J. Appl. Sci. 7(12), 1532 (2010)

    Article  Google Scholar 

  32. Gonalves, L., Novo, J., Campilho, A.: Hessian based approaches for 3d lung nodule segmentation. Expert Syst. Appl. 61, 1–15 (2016)

    Google Scholar 

  33. Gould, M.K., et al.: Evaluation of individuals with pulmonary nodules: when is it lung cancer. Chest 143(5 Suppl), 93S-120S (2013)

    Article  Google Scholar 

  34. Grigorescu, S.E., Petkov, N., Kruizinga, P.: Comparison of texmre features based on gabor filters. IEEE Trans. Image Process. 11(10), 1160–1167 (2002)

    Article  MathSciNet  Google Scholar 

  35. Gu, Y., et al.: Automated delineation of lung tumors from ct images using a single click ensemble segmentation approach. Pattern Recog. 46(3), 692–702 (2013)

    Article  Google Scholar 

  36. Gudise, V. G., Venayagamoorthy, G. K.: Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks. In Swarm Intelligence Symposium. IEEE (2003)

    Google Scholar 

  37. Hua, P., Song, Q., Sonka, M., Hoffman, E. A., Reinhardt, J. M.: Segmentation of pathological and diseased lung tissue in ct images using a graph-search algorithm. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE (2011)

    Google Scholar 

  38. Jacobs, C., Murphy, K., Twellmann, T., de Jong, P. A., van Ginneken, B.: Computer- aided detection of solid and ground glass nodules in thoracic ct images using two independent cad systems. In: The Fourth International Workshop on Pulmonary Image Analysis (2011)

    Google Scholar 

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

    Article  Google Scholar 

  40. Shen, S., Sandham, W., Granat, M., Sterr, A.: Mri fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans. Inf. Technol. Biomed. 9(3), 459–467 (2005)

    Article  Google Scholar 

  41. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference on. IEEE (1998)

    Google Scholar 

  42. Shih-Chung, B.L., Freedman, M.T., Lin, J.-S., Mun, S.K.: Automatic lung nodule detection using profile matching and back-propagation neural network techniques. J. Dig. Imaging 6(1), 48–54 (1993)

    Article  Google Scholar 

  43. Yuan, J.: Active contour driven by local divergence energies for ultrasound image segmentation. IET Image Process. 7(3), 252–259 (2013)

    Article  MathSciNet  Google Scholar 

  44. Zhou, S., Cheng, Y., Tamura, S.: Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest ct images. Biomed. Sig. Process. Control 13, 62–67 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Maharaja Institute of Technology, Mysore, India

    Y. H. Sharath Kumar & K. P. Smithashree

Authors
  1. Y. H. Sharath Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. P. Smithashree
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Y. H. Sharath Kumar .

Editor information

Editors and Affiliations

  1. University of Mysore, Mysore, India

    D. S. Guru

  2. Maharaja Institute of Technology Mysore, Mandya, India

    Sharath Kumar Y. H.

  3. Maharaja Institute of Technology Mysore, Mandya, India

    Balakrishna K.

  4. Jawaharlal Nehru University, New Delhi, India

    R. K. Agrawal

  5. Tokyo Denki University, Tokyo, Japan

    Manabu Ichino

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumar, Y.H.S., Smithashree, K.P. (2022). An Automated CAD System for Classification of Lung Module. In: Guru, D.S., Y. H., S.K., K., B., Agrawal, R.K., Ichino, M. (eds) Cognition and Recognition. ICCR 2021. Communications in Computer and Information Science, vol 1697. Springer, Cham. https://doi.org/10.1007/978-3-031-22405-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-22405-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22404-1

  • Online ISBN: 978-3-031-22405-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation