Purpose: Considering that false-positive and true pulmonary nodules are highly similar in shapes and sizes between lung computed tomography scans, we develop and evaluate a false-positive nodules reduction method applied to the computer-aided diagnosis system.
Methods: To improve the pulmonary nodule diagnosis quality, a 3D convolutional neural networks (CNN) model is constructed to effectively extract spatial information of candidate nodule features through the hierarchical architecture. Furthermore, three paths corresponding to three receptive field sizes are adopted and concatenated in the network model, so that the feature information is fully extracted and fused to actively adapting to the changes in shapes, sizes, and contextual information between pulmonary nodules. In this way, the false-positive reduction is well implemented in pulmonary nodule detection.
Results: Multi-path 3D CNN is performed on LUNA16 dataset, which achieves an average competitive performance metric score of 0.881, and excellent sensitivity of 0.952 and 0.962 occurs to 4, 8 FP/Scans.
Conclusion: By constructing a multi-path 3D CNN to fully extract candidate target features, it accurately identifies pulmonary nodules with different sizes, shapes, and background information. In addition, the proposed general framework is also suitable for similar 3D medical image classification tasks.
Keywords: 3D convolutional neural networks; Computer-aided diagnosis system; Deep learning; False-positive reduction; Pulmonary nodule detection.
© 2021. CARS.