Collaborative Classifiers in CT Colonography CAD

摘要

Multiple classifiers working collaboratively can usually achieve better performance than any single classifier working independently. Our CT colonography computer-aided detection (CAD) system uses support vector machines (SVM) as the classifier. In this paper, we developed and evaluated two schemes to collaboratively apply multiple SVMs in the same CAD system. One is to put the classifiers in a sequence (SVM sequence) and apply them one after another; the other is to put the classifiers in a committee (SVM committee) and use the committee decision for the classification. We compared the sequence order (best-first, worst-first and random) in the SVM sequence and two decision functions in the SVM committee (majority vote and sum probability). The experiments were conducted on 786 CTC datasets, with 63 polyp detections. We used 10-fold cross validation to generate the FROC curves, and conducted 100 bootstraps to evaluate the performance variation. The result showed that collaborative classifiers performed much better than individual classifiers. The SVM sequence had slightly better accuracy than the SVM committee but also had bigger performance variation.