00000cab a22000003a 4500 UP-99796217609532961 Buklod 20231007234406.0 m |o d | ta 101110s xx d | ||r ||||| || DENGII eng Boroczky, L. Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD. pp. 504-511 We propose a feature subset selection method based on genetic algorithms to improve the performance of false positive reduction in lung nodule computer-aided detection (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 (52 true nodules and 443 false ones) acquired by multislice CT scans. From 23 features calculated for each detected structure, the suggested method determined ten to be the optimal feature subset size, and selected the most relevant ten features. A support vector machine classifier trained with the optimal feature subset resulted in 100% sensitivity and 56.4% specificity using an independent validation set. Experiments show significant improvement achieved by a system incorporating the proposed method over a system without it. This approach can be also applied to other machine learning problems; e.g. computer-aided diagnosis of lung nodules CAD. False positive reduction. Feature subset selection. Genetic algorithms. Lung nodule computer-aided detection. Lung nodule database. Medical decision making. Multislice CT scans. Supervised machine learning. Support vector machines. IEEE Transactions on information technology in biomedicine 10, 3 (2006). FO Article