Table 1 Performance of proposed RGD model in 10-fold cross-validation experiments
From: A novel fusion algorithm for benign-malignant lung nodule classification on CT images
Folds | Accuracy | Sensitivity | Specificity | Precision | F1 Score | AUC |
|---|---|---|---|---|---|---|
1 | 93.83% | 84.38% | 100% | 100% | 0.9153 | 0.9566 |
2 | 95.00% | 90.63% | 97.92% | 96.67% | 0.9355 | 0.9785 |
3 | 95.00% | 96.77% | 93.88% | 90.91% | 0.9375 | 0.9921 |
4 | 88.61% | 87.10% | 89.58% | 84.38% | 0.8571 | 0.9516 |
5 | 90.91% | 82.76% | 95.83% | 82.31% | 0.8727 | 0.9231 |
6 | 93.59% | 90.00% | 95.83% | 93.10% | 0.9153 | 0.9701 |
7 | 96.20% | 90.32% | 100% | 100% | 0.9492 | 0.9758 |
8 | 94.87% | 93.55% | 95.74% | 93.55% | 0.9355 | 0.9629 |
9 | 92.31% | 83.33% | 97.92% | 96.15% | 0.8929 | 0.9688 |
10 | 92.21% | 93.33% | 91.49% | 87.50% | 0.9032 | 0.9496 |
Mean | \(93.25\% \pm 0.021\) | \(89.22\% \pm 0.045\) | \(95.82\% \pm 0.032\) | \(92.46\% \pm 0.058\) | \(0.9114 \pm 0.029\) | \(0.9629 \pm 0.018\) |