  Lung nodule classification is a class imbalanced problem because nodules are
found with much lower frequency than non-nodules. In the class imbalanced
problem, conventional classifiers tend to be overwhelmed by the majority class
and ignore the minority class. We therefore propose cascaded convolutional
neural networks to cope with the class imbalanced problem. In the proposed
approach, multi-stage convolutional neural networks that perform as
single-sided classifiers filter out obvious non-nodules. Successively, a
convolutional neural network trained with a balanced data set calculates nodule
probabilities. The proposed method achieved the sensitivity of 92.4\% and 94.5%
at 4 and 8 false positives per scan in Free Receiver Operating Characteristics
(FROC) curve analysis, respectively.
