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Lung nodule classification is a class imbalanced problem because nodules arefound with much lower frequency than non-nodules. In the class imbalancedproblem, conventional classifiers tend to be overwhelmed by the majority classand ignore the minority class. We therefore propose cascaded convolutionalneural networks to cope with the class imbalanced problem. In the proposedapproach, multi-stage convolutional neural networks that perform assingle-sided classifiers filter out obvious non-nodules. Successively, aconvolutional neural network trained with a balanced data set calculates noduleprobabilities. 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.

Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images