Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks
This addresses the problem of class imbalance in medical imaging for lung nodule detection, offering incremental improvements over prior methods.
The paper tackled lung nodule classification in class-imbalanced data by combining a Fusion classifier with cascaded convolutional neural networks, achieving sensitivities of 94.4% and 95.9% at 4 and 8 false positives per scan in FROC analysis.
Lung nodule classification is a class imbalanced problem, as 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 showed that cascaded convolutional neural networks can classify the nodule candidates precisely for a class imbalanced nodule candidate data set in our previous study. In this paper, we propose Fusion classifier in conjunction with the cascaded convolutional neural network models. To fuse the models, nodule probabilities are calculated by using the convolutional neural network models at first. Then, Fusion classifier is trained and tested by the nodule probabilities. The proposed method achieved the sensitivity of 94.4% and 95.9% at 4 and 8 false positives per scan in Free Receiver Operating Characteristics (FROC) curve analysis, respectively.