Table 1.
Overview of published works regarding nodule detection approaches in lung CT images (2020–2021).
| Authors | Year | Dataset | Methods | Performance Results (%) |
|---|---|---|---|---|
| Tan et al. [78] | 2020 | LIDC-IDRI | 3D CNNs, based on FCN, DenseNet, and U-Net | TPR = 97.5 |
| Mukherjee et al. [88] | 2020 | LIDC-IDRI | Ensemble stacking | ACC = 99.5 TPR = 99.2 TNR = 98.8 FPR = 1.09 FNR = 0.85 |
| Shi et al. [79] | 2020 | LUNA16 | 3D Res-I and U-Net network | TPR = 96.4 FROC = 83.7 |
| Khehrah et al. [86] | 2020 | LIDC-IDRI | SVM | ACC = 92 TPR = 93.7 TNR = 91.2 PPV = 83.3 MCC = 83.8 |
| Kuo et al. [87] | 2020 | LIDC-IDRI Private (320 patients) | SVM | TPR = 92.1 |
| Zheng et al. [80] | 2020 | LIDC-IDRI | 3D multiscale dense CNNs | TPR = 94.2 (1.0 FP/scan), 96.0 (2.0 FPs/image) |
| Paing et al. [89] | 2020 | LIDC-IDRI | Optimized random forest | ACC = 93.1 TPR = 94.9 TNR = 91.4 |
| Liu et al. [100] | 2020 | LIDC-IDRI | CNN algorithm: You Only Look Once v3 | TPR = 87.3 |
| Harsono et al. [97] | 2020 | LIDC-IDRI Private (546 patients) | I3DR-Net | mAP = 49.6 (LIDC), 22.9 (private) AUC = 81.8 (LIDC), 70.4 (private) |
| Xu et al. [81] | 2020 | LUNA16 | 3D CNN networks: V-Net and multi-level contextual 3D CNNs | TPR = 93.1 (1.64 FP/scan) CPM = 75.7 |
| Drokin and Ericheva [96] | 2020 | LIDC-IDRI | Algorithm for sampling points from a point cloud | FROC = 85.9 |
| El-Regaily et al. [90] | 2020 | LIDC-IDRI | Multi-view CNN | ACC = 91.0 TPR = 96.0 TNR = 87.3 F-score = 78.7 |
| Ye et al. [82] | 2020 | LUNA16 | Three modified V-Nets with multilevel receptive fields | ACC = 66.7 TPR = 81.1 PPV = 78.1 F-score = 78.7 |
| Baker and Ghadi [93] | 2020 | LIDC-IDRI | SVM | NRR = 94.5 FPR = 7 cluster/image |
| Halder et al. [94] | 2020 | LIDC-IDRI | SVM | ACC = 88.2 TPR = 86.9 TNR = 86.9 |
| Jain et al. [83] | 2020 | LUNA16 | SumNet | ACC = 94.1 TNR = 94.0 DSC = 93.0 |
| Mahersia et al. [95] | 2020 | LIDC-IDRI | SVM, Bayesian back-propagation neuronal classifier and neuro-fuzzy classifier | NRR = 97.9 (neuronal classifier), 97.3 (SVM), 94.2 (neuro-fuzzy classifier) |
| Mittapalli and Thanikaiselvan [91] | 2021 | LUNA16 | Multiscale CNN with Compound Fusions | CPM = 94.8 |
| Vipparla et al. [92] | 2021 | LUNA16 | 3D Attention-based CNN architectures: MP-ACNN1, MP-ACNN2 and MP-ACNN3 | CPM = 93.1 |
| Luo et al. [84] | 2021 | LUNA16 | SCPM-Net | TPR = 92.2 (1 FPs/image), 93.9 (2 FPs/image), 96.4 (8FPs/image) |
| Bhaskar and Ganashree [85] | 2021 | DSB-2017 | Gaussian mixture convolutional auto encoder + 3D deep CNN | ACC = 74.0 |
ACC: accuracy; AUC: area under the ROC curve; CPM: competition performance metric; DSC: Sørensen–Dice coefficient; FDR: false discovery rate; FNR: false negative rate; FP: false positive; FPR: false positive rate; FROC: free-response receiver operating characteristic; mAP: mean average precision; MCC: Matthews correlation coefficient; NPV: negative predictive value; NRR: nodule recognition rate; PPV: positive predictive value; TNR: true negative rate; TPR: true positive rate.