Table 2 Comparison of segmentation performance across different models without data augmentation.
Method | Year | Params (M) | FLOPs (G) | MJ | GMH | COVID | Shenzhen set | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
DSC | JI | DSC | JI | DSC | JI | DSC | JI | ||||
U-Net11 | 2015 | 7.76 | 24.1 | 0.9546 ± 1.93 | 0.9214 ± 2.88 | 0.8475 ± 2.38 | 0.7479 ± 3.39 | 0.9140 ± 0.98 | 0.8510 ± 1.35 | 0.9168 ± 1.05 | 0.8519 ± 1.60 |
U-Net++13 | 2018 | 9.03 | 58.4 | 0.9543 ± 2.76 | 0.9248 ± 3.62 | 0.8610 ± 1.59 | 0.7650 ± 2.21 | 0.9291 ± 0.56 | 0.8718 ± 0.86 | 0.9250 ± 0.34 | 0.8647 ± 0.47 |
AttentionUNet16 | 2018 | 0.84 | 16.5 | 0.9570 ± 2.08 | 0.9257 ± 2.82 | 0.8524 ± 1.83 | 0.7512 ± 2.65 | 0.9284 ± 0.27 | 0.8699 ± 0.48 | 0.9197 ± 0.61 | 0.8550 ± 1.05 |
ResUNet++12 | 2019 | 4.07 | 32.0 | 0.9655 ± 1.04 | 0.9367 ± 1.60 | 0.8598 ± 2.45 | 0.7679 ± 3.58 | 0.9317 ± 0.40 | 0.8765 ± 0.55 | 0.9217 ± 0.81 | 0.8619 ± 1.23 |
DC-UNet18 | 2021 | 10.06 | 39.2 | 0.9570 ± 2.69 | 0.9288 ± 3.40 | 0.8303 ± 0.74 | 0.7221 ± 1.24 | 0.9256 ± 0.99 | 0.8666 ± 1.56 | 0.9107 ± 1.04 | 0.8417 ± 1.68 |
DCSAU-Net19 | 2023 | 2.59 | 6.88 | 0.9632 ± 1.59 | 0.9342 ± 2.35 | 0.8336 ± 1.64 | 0.7430 ± 2.51 | 0.9220 ± 1.31 | 0.8647 ± 1.75 | 0.9255 ± 0.57 | 0.8683 ± 0.88 |
I2U-Net20 | 2024 | 6.75 | 3.57 | 0.9590 ± 2.10 | 0.9297 ± 2.79 | 0.8856 ± 1.17 | 0.8080 ± 1.58 | 0.9352 ± 0.57 | 0.8823 ± 0.87 | 0.9270 ± 0.75 | 0.8710 ± 1.02 |
AMRU++ (Proposed) | 10.65 | 51.0 | 0.9628 ± 1.67 | 0.9346 ± 2.44 | 0.9097 ± 1.54 | 0.8403 ± 2.36 | 0.9422 ± 0.45 | 0.8930 ± 0.74 | 0.9338 ± 0.38 | 0.8797 ± 0.66 | |