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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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 978-4244-4764-0/09
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)
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)
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)
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 2231-2803
Li, Q.: Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Comput. Med. Imag. Grap. 31(4), 248–257 (2007)
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)
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)
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)
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 Engineering
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)
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)
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)
Singh, R., Khare, A.: Fusion of multimodal medical images using daubechies complex wavelet transform: a multiresolution approach. Inf. Fusion 19(1), 49–60 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Arulmurugan, R., Anandakumar, H. (2018). Early Detection of Lung Cancer Using Wavelet Feature Descriptor and Feed Forward Back Propagation Neural Networks Classifier. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-71767-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71766-1
Online ISBN: 978-3-319-71767-8
eBook Packages: EngineeringEngineering (R0)