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Lung Cancer Classification using Curvelet Transform and Neural Network with Radial Basis Function
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 120 - Number 13 |
| Year of Publication: 2015 |
| Authors: M. Obayya, Mohamed Ghandour |
10.5120/21290-4267
|
M. Obayya, Mohamed Ghandour . Lung Cancer Classification using Curvelet Transform and Neural Network with Radial Basis Function. International Journal of Computer Applications. 120, 13 ( June 2015), 33-37. DOI=10.5120/21290-4267
Abstract
In this paper, computed tomographic (CT) chest images were investigated to develop an automated system to discriminate lung cancer. These were done by analyzing Data recorded for patients with benign cancer, and also patients with malignant lung cancer were taken in account. The techniques for utilized feature extraction included features derived from texture analysis based on Gray Level Co-occurrence Matrix (GLCM) of the input image, as well as features derived from curvelet transform-based features. An artificial neural network (ANN) with radial basis function classifier was utilized to classify the type of cancer whether benign or malignant. The results have shown that using curvelet domain features gives the highest rate to recognize lung cancer. Classification correct rate is up to 96%.
References
- American Cancer Society, Cancer Facts and Figureures 2014. Atlanta, GA:American Cancer Society, 2014
- H. Fujita S. Itoh Y. Lee, T. Hara and T. Ishigaki. "Automated detection of pulmonary nodules in helical ct images based on an improved template-matching technique". IEEE Transactions on Medical Imaging, 20, 2001.
- M. Prokop I. Sluimer, A. Schilham and B. van Ginneken. "Computer analysis of computed tomography scans of the lung: A survey". IEEE Transactions on Medical Imaging, 25(4):385–405, April 2006.
- E. Dandil, M. Cakiroglu, Z. Eksi, M. ozkan, Oz. Kar Kurt, Ar. Canan, "Artificial Neural Network-Based Classification System for Lung Nodules on Computed Tomography Scans", International Conference of Soft Computing and Pattern Recognition, 978-1-4799-5934-1/14/$31. 00 ©2014 IEEE
- Yongjun WU, Na Wang, Hongsheng Zhang, Lijuan Qin, Zhen YAN, Yiming WU," Application of Artificial Neural Networks in the Diagnosis of Lung Cancer by Computed Tomography", Sixth International Conference on Natural Computation,2010.
- J. Kuruvilla, K. Gunavathi, "Lung cancer classification using neural networksfor CT images", Computer Methods and Programs in Biomedicine, vol. 113, pp. 202–209, 2014.
- Amal Farag, Asem Ali, James Graham, Aly Farag, Salwa Elshazly and Robert Falk, "Evaluation of geometric feature descriptors for detection and classification of Lung Nodules in low dose CT scans of the chest" ,IEEE transactions, PP. 169-172, 2011.
- R. C. Hardie, S. K. Rogers, T. Wilson, and A. Rogers, "Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs " Medical Image Analysis vol. 12, pp. 240- 258, 2008.
- Lung Image Database Consortium (LIDC): https://imaging. nci. nih. gov/ncia/login. jsf
- Tinku A. , Ajoy K. , "Image Processing Principles and applications," John Wiley & Sons, Inc. , Hoboken, New Jersey, 2005
- Sasidhar B, Ramesh Babu D R, Bhaskarao N, Basheer Jan, "Automated Segmentation of Lung Regions and Detection of Lung Cancer in CT scan", International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013.
- Randy Crane, A simplied approach to Image Processing: classical and modern technique in C. Prentice Hall, ISBN0-13-226616-1, 1997.
- A. Eleyan and H. Demirel, "Co-occurrence matrix and its statistical features as a new approach for face recognition", Turk J Elec Eng & Comp Sci. , Vol. 19, No. 1, pp. 97-107, 2011.
- E. J. Candes, L. Demanet, D. L. Donoho, L. Ying, "Fast Discrete Curvelet Transforms" Technical Report, Cal Tech, 2005.
- Brijesh K. Verma, Handwritten Hindi Character Recognition Using Multilayer Perceptron and RBF Neural Networks, IEEE Trans. On Neural Networks, Vol. 21, August 2004, pp. 2111-2115.
- Hanan M. Amer, Fatma E. Z. Abou-Chadi, and Marwa I. Obayya,"A Computer-Aided System for Classifying Computed Tomographic (CT) Lung Images Using Artificial Neural Network and Data Fusion", IJCSNS International Journal of Computer Science and Network Security, VOL. 11 No. 10, October 2011.
- Sebastian PINTEA & Ramona Molodovan "The receiver-operating characteristic (roc) analysis: fundamentals and applications in clinical psychology" Journal of Cognitive and Behavioral Psychotherapies, Vol. 9, No. 1, March 2009, 49-66.
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