Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy C-Means Clustering Algorithm Based on Kernel Method
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
An analysis of different clustering algorithms for ROI detection in high resolutions CT lung images
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
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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).