Advances, Systems and Applications
Table 2 Sensitivity comparison of different algorithms with proposed methods
From: Feature-enhanced fusion of U-NET-based improved brain tumor images segmentation
Method | MSD | BRATS |
|---|---|---|
UNET | 45.21 | 90.19 |
Dense UNET | 46.27 | 91.46 |
Att -UNET | 50.08 | 85.24 |
UNET +  +  | 49.0 | 89.06 |
UNET3 +  | 44.9 | 89.30 |
Trans-UNET | 47.22 | 92.34 |
TransU2-UNET | 47.45 | 93.85 |
UNET + CLAHE | 97.12 | 98.36 |
UNET + MBOBHE | 97.42 | 99.50 |
UNET + MPHE | 97.63 | 77.72 |
FE1-UT | 97.96 | 99.49 |
FE2-UT | 97.8 | 91.73 |
FE3-UT | 97.64 | 63.16 |