Abstract
A machine learning based classification technique to diagnose lung CT scan images as cancerous or noncancerous is proposed in this paper. Lung cancer is regarded as one of the major fatal disease among the population throughout the world. Early diagnosis of lung cancer can be an important factor which can decrease the death rate among people. In large medical organizations manual inspection of CT scan, MRI images etc. puts a lot of workload on doctors and radiologist. An effective diagnosis technique can really reduce their efforts. CT (Computerized Tomography) scan images are used in medical field to analyze various parts of body. Grey scale CT scan images are used here as dataset, image preprocessing and feature of images are used. SVM classifiers are used for diagnosis. The main objective of this paper is to improve accuracy rate for lung cancer diagnosis by designing a hybrid SVM.
This is a preview of subscription content, log in via an institution to check access.
References
Nandpuru, H.B., Salankar, S.S., Bora, V.R.: MRI brain cancer classification using support vector machine. In: IEEE Students’ Conference on Electrical, Electronics and Computer Science (2014)
Taher, F., Werghi, N., Al-Ahmad, H.: Bayesian classification and artificial neural network methods for lung cancer early diagnosis. IEEE (2012)
Zhang, Y., Wu, L.: An MR brain images classifier via principal component analysis and kernel support vector machine. Progress Electromagnet. Res. 130(369), 388 (2012)
Sudha, V., Jayashree, P.: Lung nodule detection in CT images using thresholding and morphological operations. Int. J. Emerg. Sci. Eng. (IJESE) 1(2) (2012). ISSN 2319–6378
Song, D., Zhukov, T.A., Markov, O., Qian, W., Tockman, M.S.: Prognosis of stage i lung cancer patients through quantitative analysis of centrosomal features. IEEE (2012)
Othman, M.F.B., Abdullah, N.B., Kamal, N.F.B.: MRI brain classification using support vector machine. IEEE (2011)
Qiao, Z., Zhou, L., Huang, J.: Sparse: linear discriminant analysis with application to high dimension low sample size data. IAENG Int. J. Appl. Math. 39, 48–60 (2009)
Jia, T., Zhao, D.Z., Yang, J.Z., Wang, X.: Automated detection of pulmonary nodules in HRCT images. IEEE (2007)
Kumar, K., Bhattacharya, S.: Artificial neural network vs linear discriminant analysis in credit ratings forecast: a comparative study of prediction performances. Rev. Account. Finance 5 (2006)
Godbole, S.: Inter-class relationships in text classification, Ph.D. Thesis, lIT, Bombay, India (2006)
Huang, Ming, Kecman, Vojislav: Gene extraction for cancer diagnosis by support vector machines. Artif. Intell. Med. 35, 185–194 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Trivedi, A., Shukla, P. (2016). Lung Cancer Diagnosis by Hybrid Support Vector Machine. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_22
Download citation
DOI: https://doi.org/10.1007/978-981-10-3433-6_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3432-9
Online ISBN: 978-981-10-3433-6
eBook Packages: Computer ScienceComputer Science (R0)