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Lung nodule classification is a class imbalanced problem, as 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 showed that cascaded convolutional neuralnetworks can classify the nodule candidates precisely for a class imbalancednodule candidate data set in our previous study. In this paper, we proposeFusion classifier in conjunction with the cascaded convolutional neural networkmodels. To fuse the models, nodule probabilities are calculated by using theconvolutional neural network models at first. Then, Fusion classifier istrained and tested by the nodule probabilities. The proposed method achievedthe sensitivity of 94.4% and 95.9% at 4 and 8 false positives per scan in FreeReceiver Operating Characteristics (FROC) curve analysis, respectively.

Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks