Acta Optica Sinica, Volume. 40, Issue 24, 2410002(2020)
Method for Identifying Benign and Malignant Pulmonary Nodules Combing Deep Convolutional Neural Network and Hand-Crafted Features
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Fig. 2. Schematic of nodule fusion method. (a)-(d) Pulmonary nodule areas are manually segmented by four radiologists; (e) pulmonary nodule area segmented by nodule fusion method
Fig. 6. Four typical nodules in LIDC-IDRI database. (a)-(d) 2D slices of nodules; (e)-(h) 3D displays of corresponding nodules
Table 1. Description of experiment setting
View tableTable 1. Description of experiment setting
Experiment Sub experiment Description a Classification based on traditional hand-crafted features 1 b Classification based on 3D-Inception-ResNet model c Classification combining CNN features and hand-crafted features d Classification on the Shanghai Chest Hospital dataset 2 Classification under three different sample configuration schemes 3 Contrast of different architectures (including DenseNet and ResNet)
Table 2. Configuration scheme of three pulmonary nodule samples
View tableTable 2. Configuration scheme of three pulmonary nodule samples
Description Configuration Number of benign nodules Number of malignant nodules ‘1’, ‘2’ as benign and ‘4’, ‘5’ as malignant 1 380 300 ‘1’, ‘2’, ‘3’ as benign and ‘4’, ‘5’ as malignant 2 736 300 ‘1’, ‘2’ as benign and ‘3’,‘4’, ‘5’ as malignant 3 380 656
Table 3. Comparison of classification results in experiment 1
View tableTable 3. Comparison of classification results in experiment 1
Method A /% SEN /% SPE /% AUC /% Hand-crafted features+Gaussian-NB 89.69 85.18 91.43 95.19 Hand-crafted features+KNN 90.72 83.33 94.03 95.54 Hand-crafted features+RF 91.64 89.42 93.06 96.78 Hand-crafted features+LDA 91.75 86.21 94.12 96.18 Hand-crafted features+SVM 91.81 88.66 94.59 96.53 3D-Inception-ResNet 91.44 92.87 91.09 96.27 Combined features+Gaussian-NB 91.75 86.21 94.11 95.99 Combined features+KNN 91.81 88.66 94.59 96.53 Combined features+RF 92.75 92.12 93.34 97.11 Combined features+LDA 90.72 88.46 91.55 96.46 Combined features+SVM 94.98 90.02 97.03 97.43
Table 4. Classification results of SCH dataset
View tableTable 4. Classification results of SCH dataset
Dataset A /% SEN /% SPE /% AUC /% LIDC-IDRI 94.98 90.02 97.03 97.43 SCH 90.91 88.10 95.83 95.58
Table 5. Comparison of classification results in three different sample configuration schemes
View tableTable 5. Comparison of classification results in three different sample configuration schemes
Configuration Method A /% SEN /% SPE /% AUC /% Configuration 1 Combined features+RF 92.75 92.12 93.34 97.11 Combined features+SVM 94.89 90.02 97.03 97.43 Configuration 2 Combined features+RF 86.04 70.87 90.20 91.70 Combined features + SVM 85.86 70.20 90.55 91.12 Configuration 3 Combined features+RF 80.38 85.11 72.37 87.26 Combined features+SVM 79.73 82.35 74.91 85.31
Table 6. Comparison of classification results of different CNN architectures
View tableTable 6. Comparison of classification results of different CNN architectures
Architecture A /% SEN /% SPE /% AUC /% 3D-DenseNet 89.69 87.10 94.55 92.64 3D-DenseNet combining hand-crafted features 90.72 90.00 89.47 94.20 3D-ResNet 84.69 76.74 90.91 88.13 3D-ResNet combining hand-crafted features 90.82 90.48 91.07 95.38 3D-Inception-ResNet 91.44 92.87 91.09 96.27 3D-Inception-ResNet combining hand-crafted features 94.98 90.02 97.03 97.43
Table 7. Comparison of the results of different methods
View tableTable 7. Comparison of the results of different methods
Method Number of nodules A /% SEN /% SPE /% AUC /% Method in Ref.[26] 664 93.20 87.90 98.50 97.10 Method in Ref.[27] 1226 85.62±2.37 81.21±6.20 89.56±1.17 90.45±2.58 Proposed method 1036 94.98 90.02 97.03 97.43
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Dachuan Gao, Shengdong Nie. Method for Identifying Benign and Malignant Pulmonary Nodules Combing Deep Convolutional Neural Network and Hand-Crafted Features[J]. Acta Optica Sinica, 2020, 40(24): 2410002
Paper Information
Category: Image Processing
Received: Jul. 13, 2020
Accepted: Sep. 15, 2020
Published Online: Nov. 23, 2020
The Author Email: Nie Shengdong (nsd4647@163.com)
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