Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD.

Feature Subset Selection for Improving the Performance of False Positive Reduction in Lung Nodule CAD.

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...

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Detalles Bibliográficos
Publicado en:IEEE Transactions on information technology in biomedicine 10, 3 (2006).
Autor principal: Boroczky, L.
Formato: Artículo
Lenguaje:English
Materias:
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.