Table 5.
Performance metrics for segmentation of classical texture analysis methods (U-Net, Gray-Level Co-occurrence Matrix, and Local Binary Pattern) evaluated with and without data augmentation on the Polyp dataset.
| Model | Accuracy (%) | Recall (%) | Specificity (%) | Dice (%) | IoU (%) |
|---|---|---|---|---|---|
| U-Net (Augmentation) | 95.00 | 99.47 | 90.00 | 94.5 ± 0.35 | 90.2 ± 0.41 |
| U-Net (No Augmentation) | 98.00 | 99.00 | 98.00 | 92.3 ± 0.41 | 87.7 ± 0.46 |
| LBP (Augmentation) | 96.50 | 99.00 | 89.00 | 90.1 ± 0.45 | 84.8 ± 0.51 |
| LBP (No Augmentation) | 98.00 | 99.49 | 96.00 | 88.0 ± 0.48 | 82.3 ± 0.53 |
| GLCM (Augmentation) | 94.50 | 99.47 | 88.00 | 86.2 ± 0.50 | 79.9 ± 0.56 |
Results highlight that U-Net and LBP methods performed exceptionally well, with accuracy rates exceeding 95%. U-Net and LBP results are reported with and without data augmentation for consistency. Bold values indicate the best results obtained.