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9 March 2011 Automated detection of pulmonary nodules in CT: false positive reduction by combining multiple classifiers
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Abstract
The purpose of this study was to investigate the usefulness of various classifier combination methods for improving the performance of a CAD system for pulmonary nodule detection in CT. We employed CT cases in the publicly available lung image database consortium (LIDC) dataset, which included 85 CT cases with 110 nodules. We first used six individual classifiers for nodule detection in CT, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN), and three types of support vector machines (SVM). Five informationfusion methods were then employed to combine the classifiers' outputs for improving detection performance. The five combination methods included two supervised (likelihood ratio method and neural network) and three unsupervised ones (the mean, the product, and the majority-vote of the output scores from the six individual classifiers). Leave-one-caseout was employed to train and test individual classifiers and supervised combination methods. At a sensitivity of 80 %, the numbers of false positives per case for the six individual classifiers were 6.1 for LDA, 19.9 for QDA, 8.6 for ANN, 23.7 for SVM-dot, 17.0 for SVM-poly, and 23.35 for SVM-ANOVA; the numbers of false positives per case for the five combination methods were 3.4 for the majority-vote rule, 6.2 for the mean, 5.7 for the product, 9.7 for the neural network, and 28.1 for the likelihood ratio method. The majority-vote rule achieved higher performance levels than other combination methods. It also achieved higher performance than the best individual classifier, which is not the case for other combination methods.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jorge Juan Suárez-Cuenca, Wei Guo, and Qiang Li "Automated detection of pulmonary nodules in CT: false positive reduction by combining multiple classifiers", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796338 (9 March 2011); https://doi.org/10.1117/12.878793
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