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Lung nodule detection is a class imbalanced problem because nodules are foundwith much lower frequency than non-nodules. In the class imbalanced problem,conventional classifiers tend to be overwhelmed by the majority class andignore the minority class. We therefore propose cascaded convolutional neuralnetworks to cope with the class imbalanced problem. In the proposed approach,cascaded convolutional neural networks that perform as selective classifiersfilter out obvious non-nodules. Successively, a convolutional neural networktrained with a balanced data set calculates nodule probabilities. The proposedmethod achieved the detection sensitivity of 85.3% and 90.7% at 1 and 4 falsepositives per scan in FROC curve, respectively.

Cascaded Neural Networks with Selective Classifiers and its evaluation using Lung X-ray CT Images