Study of Various Neural Networks to Improve the Defuzzification of Fuzzy Clustering Algorithms for ROIs Detection in Lung CTs
The detection of pulmonary nodules in CT images has been extensively researched because it is a highly complicated and socially interesting matter. The classical approach consists in the development of a computer-aided diagnosis (CAD) system that indicates, in phases, the presence or absence of nodules. A common phase of these systems is the detection of regions of interest (ROIs), that may correspond to nodules, in order to reduce the searching space. This paper evaluates the use of various neural networks for the defuzzification of the output of fuzzy clustering algorithms, in order to improve the detection of true positives and the reduction of false positives. Also, they are compared to the results from a support vector machine (SVM).
KeywordsSupport Vector Machine False Positive Rate True Positive Rate Radial Basis Function Neural Network Fuzzy Cluster Algorithm
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- 2.Lee, Y., Hara, T., Fujita, H., Itoh, S., Ishigaki, T.: Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Transactions on Medical Imaging 27(7), 595–604 (2001)Google Scholar
- 4.Castro, A., Bóveda, C., Rey, A., Arcay, B.: An analysis of different clustering algorithms for ROI detection in high resolutions CT lung images. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6374, pp. 241–248. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 5.Castro, A., Arcay, B.: Comparison of various fuzzy clustering algorithms in the detection of ROI in lung CT and a modified kernelized-spatial fuzzy c- means algorithm. In: Proc. of 10th IEEE Int. Conf. on Inf. Tech. and Appl. in Biom., Corfu., Greece (2010)Google Scholar
- 9.Zhong, W.D., Wei, X.X., Jian, Y.P.: Fuzzy C-Means clustering algorithm based on kernel method. In: Proceedings of the Fifth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003 (2003)Google Scholar
- 11.Weston, J., Watkins, C.: Multi-class support vector machines. In: Verleysen, M. (ed.) Proc. ESANN 1999, Brussels, Belgium (1999)Google Scholar