Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images
This work addresses lung nodule detection in medical imaging, which is critical for early cancer diagnosis, but it is incremental as it builds on existing neural network methods.
The paper tackled the class imbalance problem in lung nodule classification by proposing cascaded convolutional neural networks with single-sided classifiers to filter out non-nodules, achieving sensitivities of 92.4% and 94.5% at 4 and 8 false positives per scan, respectively.
Lung nodule classification 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, multi-stage convolutional neural networks that perform as single-sided 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 sensitivity of 92.4\% and 94.5% at 4 and 8 false positives per scan in Free Receiver Operating Characteristics (FROC) curve analysis, respectively.