TABLE 2.
The evaluation metric score comparison of our method, LRU-Net, with six other methods on the training and validation sets.
| Method | Evaluation metrics score on training data | Evaluation metrics score on validation data | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Accuracy | F1 Score | IoU | Dice Coefficient Score | Dice Loss | Boundary Dice Loss | Accuracy | F1 Score | IoU | Dice Coefficient Score | Dice Loss | Boundary Dice Loss | |
| LRU-Net | 98.87% | 97.58% | 96.45% | 98.71% | 5.13% | 8.19% | 98.83% | 98.16% | 96.40% | 97.93% | 5.25% | 8.60% |
| Attention-Unet | 94.11% | 90.61% | 83.76% | 90.19% | 26.0% | 44.22% | 94.67% | 90.72% | 83.75% | 90.10% | 26.36% | 42.91% |
| Attention-ResUnet | 95.75% | 93.06% | 89.72% | 92.51% | 20.2% | 19.70% | 95.97% | 93.38% | 88.14% | 92.38% | 19.70% | 31.88% |
| InceptionV3-Unet | 97.67% | 95.92% | 89.66% | 95.82% | 11.1% | 18.71% | 97.75% | 96.40% | 93.20% | 97.82% | 10.48% | 17.13% |
| Swin-Unet | 96.88% | 95.33% | 91.29% | 95.71% | 11.3% | 16.65% | 97.03% | 96.17% | 91.17% | 96.21% | 13.54% | 19.97% |
| Trans-Unet | 98.16% | 97.01% | 95.28% | 97.97% | 7.02% | 9.72% | 97.95% | 97.53% | 95.82% | 96.81% | 9.33% | 10.04% |
| Rethinking ResNets | 98.70% | 97.36% | 96.30% | 98.53% | 6.07% | 9.27% | 98.73% | 97.98% | 96.33% | 97.36% | 5.81% | 9.77% |
The bold values highlights that our proposed method LRU-Net achieved the highest accuracy, F1 score, IoU, Dice Coefficient Score, Dice loss, and Boundary Dice Loss on the validation data.