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- W2116503025 abstract "In this paper, we propose a feature subset selection method based on genetic algorithms to improve the performance of false positive reduction in lung nodule CAD. It is coupled with a classifier based on support vector machines. The proposed approach determines automatically the optimal size of the feature set and chooses the most relevant features from a feature pool. Its performance was tested using a lung nodule database (66 true nodules and 123 false ones) acquired by multi-slice CT scans. From 23 features calculated for each detected structure, the suggested method determined 9 as the optimal feature subset size and selected the nine features. A support vector machine-based classifier trained with the optimal feature subset has resulted in 92.4% sensitivity and 85.4% specificity using leave-one-out cross validation. Experiments also showed significant improvement achieved by a system incorporating the proposed method over a system without it. It can be also applied to other machine learning problems: e.g. computer-aided diagnosis of lung nodules." @default.
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- W2116503025 date "2005-07-27" @default.
- W2116503025 modified "2023-09-23" @default.
- W2116503025 title "Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD" @default.
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- W2116503025 doi "https://doi.org/10.1109/cbms.2005.53" @default.
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