References

Abdulla, A.A. and S.M. Shaharum, 2012. Lung cancer cell classification method using artificial neural network. Inf. Eng. Lett., 2: 52-58.
Direct Link  |  

Adlassing, K.P., 1986. Fuzzy set theory in medical diagnosis. IEEE Transac. Systems Man Cybernetics SMC, 16: 260-265.
Direct Link  |  

Al-Daoud, E., 2010. Cancer diagnosis using modified fuzzy network. Universal J. Comput. Sci. Eng. Technol., 1: 73-78.
Direct Link  |  

Camarlinghi, N., I. Gori, A. Retico, R. Bellotti and P. Bosco et al., 2012. Combination of computer-aided detection algorithms for automatic lung nodule identification. Int. J. Comput. Assisted Radiol. Surgery, 7: 455-464.
CrossRef  |  Direct Link  |  

Cascio, D., R. Magro, F. Fauci, M. Iacomi and G. Raso, 2012. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Comput. Biol. Med., 42: 1098-1109.
Direct Link  |  

Dehmeshki, J., X. Ye, X. Lin, M. Valdivieso and H. Amin, 2007. Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput. Med. Imaging Graoh., 31: 408-417.
CrossRef  |  PubMed  |  

Er, O., N. Yumusak and F. Temurtas, 2010. Chest diseases diagnosis using artificial neural networks. Expert Syst. Appl., 37: 7648-7655.
CrossRef  |  Direct Link  |  

Gurcan, M.N., B. Sahiner, N. Petrick, H. Chan, E.A. Kazerooni, P.N. Cascade and L. Hadjiiski, 2002. Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system. Med. Phys., 29: 2552-2558.
CrossRef  |  

Gutte, H., D. Jakobsson, F. Olofsson, M. Ohlsson and S. Valind et al., 2007. Automated interpretation of PET/CT images in patients with lung cancer. Nucl. Med. Commun., 28: 79-84.
CrossRef  |  Direct Link  |  

Hong, H., J. Lee and Y. Yim, 2008. Automatic lung nodule matching on sequential CT images. Comput. Biol. Med., 38: 623-634.
Direct Link  |  

Kakar, M., A. Mencattini and M. Salmeri, 2013. Extracting fuzzy classification rules from texture segmented HRCT lung images. J. Digital Imaging, 26: 227-238.
Direct Link  |  

Keshani, M., 2010. Lung nodule segmentation using active contour modeling. Proceedings of the 6th Iranian Conference on Machine Vision and Image Processing (MVIP), October 27-28, 2010, IEEE, Isfahan, Iran, ISBN:978-1-4244-9706-5, pp: 1-6.

Levner, I. and H. Zhang, 2007. Classification-driven watershed segmentation. IEEE Trans. Image Proces., 16: 1437-1445.
Direct Link  |  

Li, D.C., C.W. Liu and S.C. Hu, 2011. A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artif. Intell. Med., 52: 45-52.
Direct Link  |  

Okada, K. and U. Akdemir, 2005. Blob segmentation using joint space-intensity likelihood ratio test: Application to 3D tumor segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) Vol. 2, June 20-25, 2005, IEEE, San Diego, California, USA., ISBN:0-7695-2372-2, pp: 437-444.

Penedo, M.G, M.J. Carreira., A. Mosquera and D. Cabello, 1998. Computer-aided diagnosis: A neural-network-based approach to lung nodule detection. IEEE Trans. Med. Imaging, 17: 872-880.
CrossRef  |  

Pimentel, B.A. and R.M.D. Souza, 2013. A multivariate fuzzy C-means method. Appl. Soft Comput., 13: 1592-1607.
Direct Link  |  

Roy, T.S., N. Sirohi and A. Patle, 2015. Classification of lung image and nodule detection using fuzzy inference system. Proceedings of the International Conference on Computing, Communication and Automation (ICCCA), May 15-16, 2015, IEEE, Noida, India, ISBN:978-1-4799-8890-7, pp: 1204-1207.

Samuel, C.C., V. Saravanan and M.V. Devi, 2007. Lung nodule diagnosis from CT images using fuzzy logic. Proceedings of the International Conference on Conference on Computational Intelligence and Multimedia Applications Vol. 3, December 13-15, 2007, IEEE, Sivakasi, Tamil Nadu, India, ISBN:0-7695-3050-8, pp: 159-163.

Seker, H., M.O. Odetayo, D. Petrovic and R.N.G. Naguib, 2003. A fuzzy logic based-method for prognostic decision making in breast and prostate cancers. IEEE. Trans. Inf. Technol. Biomed., 7: 114-122.
CrossRef  |  Direct Link  |  

Sivkumar, S., 2013. Lung nodule detection using fuzzy clustering and support vector machine. Intl. J. Eng. Technol., 5: 179-185.
Direct Link  |  

Sofka, M. and C.V. Stewart, 2010. Location registration and recognition (LRR) for serial analysis of nodules in lung CT scans. Med. Image Anal., 14: 407-428.
CrossRef  |  

Suarez-Cuenca, J.J., P.G. Tahoces, M. Souto, M.J. Lado, M. Remy-Jardin, J. Remy and J.J. Vidal, 2009. Application of the iris filter for automatic detection of pulmonary nodules on computed tomographyimages. Comput. Biol. Med., 39: 921-933.
CrossRef  |  

Taylan, O. and B. Karagozoglu, 2009. An adaptive neuro-fuzzy model for prediction of student’s academic performance. Comput. Ind. Eng., 57: 732-741.
Direct Link  |  

Vieira, S.M., J.M. Sousa and U. Kaymak, 2012. Fuzzy criteria for feature selection. Fuzzy Sets Syst., 189: 1-18.
Direct Link  |  

Walia, N., S.K. Tiwari and R. Malhotra, 2015. Design and identification of tuberculosis using fuzzy based decision support system. Adv. Comput. Sci. Inf. Technol., 2: 57-62.

Wu, D., L. Lu, J. Bi, Y. Shinagawa and K. Boyer et al., 2010. Stratified learning of local anatomical context for lung nodules in CT images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 13-18, 2010, IEEE, San Francisco, California, ISBN:978-1-4244-6984-0, pp: 2791-2798.