False positive reduction for lung nodule CAD using support vector machines and genetic algorithms

Abstract In this paper, we propose a machine learning approach to reduce false positive lung nodules identified in multi-slice CT scans by CAD algorithms. From a pool of features computed from the thin-slice scans, a genetic algorithm is used to determine an optimal feature subset for training a classifier that will eliminate as many of the false positives as possible while retaining the true nodules. We use support vector machines as the classifier for its superior performance. The experiment was conducted on a database of 66 true nodules and 123 false ones. From 15 features calculated for each nodule our approach selected 9 as the optimal feature subset size and the resulting classifier trained with a selected set of 9 features was able to achieve 98.5% sensitivity and 82.9% specificity using leave-one-out cross validation.