With the development of computer-aided polyp detection towards virtual colonoscopy screening, the trade-off between
detection sensitivity and specificity has gained increasing attention. An optimum detection, with least number of false
positives and highest true positive rate, is desirable and involves interdisciplinary knowledge, such as feature extraction,
feature selection as well as machine learning. Toward that goal, various geometrical and textural features, associated
with each suspicious polyp candidate, have been individually extracted and stacked together as a feature vector.
However, directly inputting these high-dimensional feature vectors into a learning machine, e.g., neural network, for
polyp detection may introduce redundant information due to feature correlation and induce the curse of dimensionality.
In this paper, we explored an indispensable building block of computer-aided polyp detection, i.e., principal component
analysis (PCA)-weighted feature selection for neural network classifier of true and false positives. The major concepts
proposed in this paper include (1) the use of PCA to reduce the feature correlation, (2) the scheme of adaptively
weighting each principal component (PC) by the associated eigenvalue, and (3) the selection of feature combinations via
the genetic algorithm. As such, the eigenvalue is also taken as part of the characterizing feature, and the necessary
number of features can be exposed to mitigate the curse of dimensionality. Learned and tested by radial basis neural
network, the proposed computer-aided polyp detection has achieved 95% sensitivity at a cost of average 2.99 false
positives per polyp.