Objective:
False-positive nodule reduction is a crucial part of a computer-aided detection
(CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a
novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive
nodule reduction task.
Methods:
Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive
nodules. First, different models were fed with 3D cubes with different sizes for encoding
multi-level contextual information to meet the challenges of the large variations of lung nodules. In
addition, image rotation and flipping were utilized to upsample positive samples which consisted of
a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information
of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then
boost feature propagation. Finally, the optimal weighted linear combination of all model scores
obtained the best classification result in this research.
Results:
The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice
CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver
Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM)
score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive
reduction track of the LUNA16 challenge.
Conclusion:
The result shows that the proposed scheme can be significant for false-positive nodule
reduction task.
Designing Chest X-ray Datasets for Improving Lung Nodules Detection Through Convolutional Neural Networks