Table 11.
Comparative results of the proposed models and other models used with different dataset.
| Model | Accuracy (%) | Senstivity (%) | Specificity (%) | Dataset |
|---|---|---|---|---|
| Linear classifier | 66.5 | 65.2 | 67.2 | Kaggle |
| Vanilla 3DCNN | 70.5 | 59.3 | 76.1 | Kaggle |
| 3D AlexNet | 85.79 | 82.74 | 88.04 | Kaggle |
| 3D-Googlenet | 87.95 | 82.74 | 91.61 | Kaggle |
| DFCNet [24] | 86.02 | 80.91 | 83.22 | LIDC-IDRI |
| TumorNet [42] | 87.41 | 81.70 | 85.17 | LIDC-IDRI |
| CMixNet [17] | 88.79 | 93.97 | 89.83 | LIDC-IDRI |
| straight 3D-CNN + softmax classifier [16] | 90.23 | 86.40 | 93.09 | Kaggle |
| Hybrid 3D-CNN + RBF-based SVM [16] | 91.8 | 88.53 | 94.23 | Kaggle |
| 3D CMixNet + GBM [17] | 91.13 | – | – | LIDC-IDRI |
| 3D CMixNet + GBM + Biomarkers [17] | 94.17 | 94 | 91 | LIDC-IDRI |
| Ours: DeeplabV3plus(ex_65) + Mobilenet-V1_1.0_224 | 93 | 90.01 | 94.3 | Kaggle |
| Ours: DeeplabV3plus(ex_65) + Inception-V3 | 95.66 | 91.2 | 97.24 | Kaggle |