Table 10.
Benchmarking table.
| C1 |
C2 |
C3 |
C4 |
C5 |
C6 |
C7 |
C8 |
C9 |
C10 |
C11 |
C12 |
C13 |
C14 |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| R# | Author | # Patients | # Images | Image Dim | Model Types | Classification vs Segmentation |
Pruning | Model type | Dim | AE | DS | JI | BA | ACC |
| R1 | Jiang et al. | – | 1168 | 5122 | VGG16 ResNet-50 Inception v3 Inception ResNet v2 DenseNet-169 |
Classification | ✗ | SDL | 2D | ✗ | ✗ | ✗ | ✗ | 0.94 0.95 0.96 0.96 0.99 |
| R2 | Kogilavani et al. | – | ∼3873 | 2242 | VGG16 MobileNet Densenet121 Xception Efficientnet NASNet |
Classification | ✗ | SDL | 2D | ✗ | ✗ | ✗ | ✗ | 0.97 0.96 0.97 0.92 0.80 0.89 |
| R3 | Paluru et al. | 69 | ∼4339 | 5122 | AnamNet | Segmentation | ✗ | SDL | 2D | ✗ | 0.75 | ✗ | ✗ | 0.99 |
| R4 | Saood et al. | – | ∼100 | 2562 | UNet SegNet |
Segmentation | ✗ | SDL | 2D | ✗ | 0.73 0.74 |
✗ | ✗ | 0.91 0.95 |
| R5 | Cai et al. | 99 | ∼250 | – | UNet | Segmentation | ✗ | SDL | 2D | ✗ | 0.98 | 0.96 | ✗ | ✗ |
| R6 | Suri et al. (Proposed) | ∼152 | ∼9,000 | 5122 |
FCN FCN-DE FCN-GA FCN–PSO FCN-WO SegNet SegNet-DE SegNet-GA SegNet-PSO SegNet-WO |
Segmentation | ✓ | SDL | 2D | ✓ |
0.78 0.93 0.93 0.92 0.94 0.96 0.96 0.96 0.96 0.96 |
0.65 0.88 0.87 0.86 0.89 0.93 0.92 0.93 0.94 0.94 |
✓ |
0.96 0.97 0.97 0.97 0.98 0.98 0.98 0.98 0.98 0.99 |
#: number; AE: Area Error; DS: Dice Similarity; JI: Jaccard Index; BA: Bland-Altman; ACC: Accuracy; Dim: Dimension (2D vs. 3D).
R#: Row number; DE: Differential Evolution; GA: Genetic Algorithm; PSO: Particle swarm Optimization; WO: Whale Optimization.