Advertisement

Early Detection of Lung Cancer Using Wavelet Feature Descriptor and Feed Forward Back Propagation Neural Networks Classifier

  • R. Arulmurugan
  • H. AnandakumarEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 28)

Abstract

A Computed Tomography (CT) scan is the most used technique for distinguishing harmful lung cancer nodules. A Computer Aided Diagnosis (CAD) framework for the recognition of lung nodules in thoracic CT pictures is implemented. A lung nodule which can be either benign or malignant can be easily classified as the better support for treatment. The proposed work is based on using Wavelet feature descriptor and combined with an Artificial Neural network for classification. The computed statistical attributes such as Autocorrelation, Entropy, Contrast, and Energy are obtained after applying wavelet transform and used as input parameters for neural network Classifier. The NN Classifier is designed by considering training functions (Traingd, Traingda, Traingdm, and Traingdx) using feed forward neural network and feed forward back propagation network. The feed forward back propagation neural network gives better classification results than feed forward. The proposed classifier produced Accuracy of 92.6%, specificity of 100% and sensitivity of 91.2% and a mean square error of 0.978.

Keywords

Lung nodule detection Feature extraction Wavelet transform Texture feature computer tomography ANN 

References

  1. 1.
    Takiar, R., Nadayil, D., Nandakumar, A.: Projection of number of cancer cases in India (2010–2020) by cancer groups. Asian Pac. J. Cancer Prev. 11(4), 1045–1049 (2010)Google Scholar
  2. 2.
    Patil, S.A., Udupi, V.R., Kane, C.D., Wasif, A.I., Desai, J.V., Jadhav, A.N.: Geometrical and Texture Features Estimation of Lung Cancer and TB Image using Chest X-Ray Database (2009). ISBN 978-4244-4764-0/09Google Scholar
  3. 3.
    Diciotti, S., Lombardo, S., Falchini, M., Picozzi, G., Mascalchi, M.: Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. IEEE T. Bio-Med. Eng. 58(12), 3418–3428 (2011)CrossRefGoogle Scholar
  4. 4.
    Farag, A., El Munim, H., Graham, J., Farag, A.: A novel approach for lung nodules segmentation in chest CT using level sets. IEEE Trans. Image Process. 22, 5202–5213 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Choi, W., Choi, T.: Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput. Meth. Prog. Bio. 133(1), 37–54 (2014)CrossRefGoogle Scholar
  6. 6.
    Magesh, B., et al.: Computer aided diagnosis system for the identification and classification of lessions in lungs. Int. J. Comput. Trends Technol. IJCTT (2011). ISSN 2231-2803Google Scholar
  7. 7.
    Li, Q.: Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Comput. Med. Imag. Grap. 31(4), 248–257 (2007)CrossRefGoogle Scholar
  8. 8.
    Ambrosini, V., Nicolini, S., Caroli, P., Nanni, C., Massaro, A., Marzola, M., et al.: PET/CT imaging in different types of lung cancer: an overview. Eur. J. Radiol. 81(5), 988–1001 (2012)CrossRefGoogle Scholar
  9. 9.
    Van Ginneken, B., Schaefer-Prokop, C., Prokop, M.: Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiol. 261(3), 719–732 (2011)Google Scholar
  10. 10.
    Jing, Z., Bin, L., Lianfang, T.: Lung nodule classification combining rule-based and SVM. In: Li, K. (eds.) Proceedings of the IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications, pp. 1033–1036, 23–26 Sept 2010, Changsha, China. Piscataway, NJ: IEEE Computer Society (2010)Google Scholar
  11. 11.
    Kumar, S.A., Ramesh, J., Vanathi, P.T., Gunavathi, K.: Robust and automated lung nodule diagnosis from CT images based on fuzzy systems. In: Manikandan, V. (eds.) Proceedings of the IEEE International Conference on Process Automation, Control and Computing, pp. 1–6, 20–22 July 2011, Coimbatore, India. Piscataway, NJ: IEEE Women in EngineeringGoogle Scholar
  12. 12.
    Keshani, M., Azimifar, Z., Tajeripour, F., Boostani, R.: Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system. Comput. Biol. Med. 43(4), 287–300 (2013)CrossRefGoogle Scholar
  13. 13.
    Armato, S., McLennan, G., Bidaut, L., McNitt-Gray, M., Meyer, C.: 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)CrossRefGoogle Scholar
  14. 14.
    Kostis, W.J., Reeves, A.P., Yankelevitz, D.F., Henschke, C.I.: Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans. Med. Imaging 22(3), 1259–1274 (2003)CrossRefGoogle Scholar
  15. 15.
    Singh, R., Khare, A.: Fusion of multimodal medical images using daubechies complex wavelet transform: a multiresolution approach. Inf. Fusion 19(1), 49–60 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  1. 1.Department of Information TechnologyBannari Amman Institute of TechnologySathyamangalamIndia
  2. 2.Department of Computer Science and EngineeringAkshaya College of Engineering and TechnologyCoimbatoreIndia

Personalised recommendations