Guang Miao, Chaofeng Li. Detection of Pulmonary Nodules CT Images Combined with Two-Dimensional and Three-Dimensional Convolution Neural Networks[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051006
- Laser & Optoelectronics Progress
- Vol. 55, Issue 5, 051006 (2018)
Fig. 1. Flow chart of lung nodule detection system
Fig. 2. CT slice original images and U-net prediction images. (a) Original images; (b) prediction images
Fig. 3. Structure of 3D convolution neural network false positive removal system network
Fig. 4. Number distribution of nodules with different sizes
Fig. 5. Images of nodules with different sizes in the database. (a) Small nodules; (b) middle nodules; (c) big nodules
Fig. 6. Training error and test accuracy curves of 3D convolution neural network model
Fig. 7. Accuracy of average number of false positives per CT image
Fig. 8. Effect of the dimensions of the input image block on the experimental results
Fig. 9. Effect of the selection of network model on experimental results
Fig. 10. False negative nodules
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Table 1. Comparison of detection algorithms of pulmonary nodules in LIDC-IDRI database
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