Laser & Optoelectronics Progress, Volume. 57, Issue 4, 041013(2020)
Detection of Pulmonary Nodules Based on C-3D Deformable Convolutional Neural Network Model
Figures & Tables(13)
Fig. 3. Structures of 3D deformable convolution and pooling. (a) 3D deformable convolution; (b) 3D deformable pooling
Fig. 5. Classification accuracy for different learning rates and optimization functions. (a) Experimental comparison of learning rate; (b) experimental comparison of optimization function
Fig. 6. ROC curves and PRC curves of different models. (a) ROC curves; (b) PRC curves
Fig. 7. Boxes of deformable convolution layers with different numbers of features. (a) Box of AUC; (b) box of F1; (c) box of P; (d) box of R
Fig. 8. Visualization results of convolution window pixel sampling. (a) Original labeled lung images; (b) original C-3D convolutional neural network; (c) improved C-3D convolutional neural network
Table 1. Setting of parameters of convolution layer and pooling layer in C-3D deformable convolutional neural network
View tableTable 1. Setting of parameters of convolution layer and pooling layer in C-3D deformable convolutional neural network
Layer F (number of convolutional feature maps)×S (kernal size) Stride Activation function Output 3D conv1 16×(3×3×3) 1×1×1 ReLU 32×32×32 3D conv2 64×(3×3×3) 2×1×1 ReLU 16×32×32 3D pool1 64×(1×2×2) 1×2×2 — 16×16×16 3D conv3 128×(3×3×3) 1×1×1 ReLU 16×16×16 3D pool2 128×(2×2×2) 2×2×2 — 8×8×8 3D conv4 256×(3×3×3) 1×1×1 ReLU 8×8×8 3D pool3 256×(2×2×2) 2×2×2 — 4×4×4 3D conv5 512×(3×3×3) 1×1×1 ReLU 4×4×4 3D pool4 512×(2×2×2) 2×2×2 — 2×2×2 3D conv6 64×(3×3×3) 1×1×1 ReLU 1×1×1 3D conv7 1×(1×1×1) 1×1×1 Sigmoid 1×1×1 Fc (fully connected layer) — — — 1
Table 2. Dataset information
View tableTable 2. Dataset information
Number Name Number of samples Number of pulmonary nodules 1 LIDC-IDIR 888 1186 2 East Hospital of Shanghai Sixth People's Hospital 300 346 3 Hospital of Kanghua Haining Zhejiang 200 212
Table 3. Comparison of results of different convolutional neural network models
View tableTable 3. Comparison of results of different convolutional neural network models
Model AUC R P F1 C-3D 0.9422±0.0105 0.9125±0.0131 0.9335±0.0135 0.8846±0.0036 C-3D±DC 0.9513±0.0127 0.9228±0.0158 0.9387±0.0101 0.8978±0.0097 C-3D±DP 0.9326±0.0241 0.8997±0.0179 0.9254±0.0157 0.8797±0.0103 C-3D±DCP 0.9575±0.0098 0.9183±0.0096 0.9413±0.0075 0.9067±0.0058
Table 4. Results comparison of different output features in C-3D deformable convolutional neural network
View tableTable 4. Results comparison of different output features in C-3D deformable convolutional neural network
Model R P F1 AUC C-3D+DCP,F:8 0.9131±0.0125 0.9226±0.0048 0.8948±0.0107 0.9394±0.0117 C-3D±DCP,F:16 0.9183±0.0096 0.9413±0.0075 0.9067±0.0058 0.9575±0.0098 C-3D±DCP,F:32 0.9253±0.0164 0.9534±0.0104 0.9286±0.0083 0.9617±0.0131 C-3D±DCP,F:64 0.8957±0.0063 0.9113±0.0243 0.9024±0.0096 0.9339±0.0164
Table 5. Comparison of different convolutional neural networks
View tableTable 5. Comparison of different convolutional neural networks
Model AUC P R F1 Data Ref. [6] 0.8434 0.7444 0.7916 0.7673 LIDC-IDRI Ref. [7] 0.9387 0.8750 0.9243 0.8990 LIDC-IDRI Ref. [8] 0.8523 0.9045 0.9162 0.9103 LIDC-IDRI Proposed method 0.9617 0.9534 0.9239 0.9286 LIDC-IDRI Proposed method 0.9661 0.9613 0.9253 0.9378 LIDC-IDIR+Local data
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Hongyang Ruan, Zhilan Chen, Yingsheng Cheng, Kai Yang. Detection of Pulmonary Nodules Based on C-3D Deformable Convolutional Neural Network Model[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041013
Paper Information
Category: Image Processing
Received: Jul. 3, 2019
Accepted: Aug. 12, 2019
Published Online: Feb. 20, 2020
The Author Email: Chen Zhilan (791257748@qq.com)
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