  Lung nodule detection 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,
cascaded convolutional neural networks that perform as selective 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 detection sensitivity of 85.3% and 90.7% at 1 and 4 false
positives per scan in FROC curve, respectively.
