Table 5.
Comparison of the proposed methodology (Hybrid-UNet segmentation model with other existing techniques for thyroid tumor segmentation
| Investigator (s) | Number of images | Technique used for pre-processing | Segmentation model(s) | Evaluation metrics |
|---|---|---|---|---|
| Case 1: Using Original TTUS Images | ||||
| S. Zhou et al. [41] | 893 | – | MG-U-Net | Dice Coefficient -0.9 |
| J. Ma et al. [37] | 10,357 | – | Self-design | Dice ratio- 0.9 |
| Proposed method (Hybrid_UNet) | 820 | – | Hybrid-UNet | mIoU—83.1 ± 7.8 and mDC = 90.1 ± 2.9 |
| Case 2: Using pre-processed TTUS Images | ||||
| Prabal Poude et al. [47] | 416 | Median Filter and Histogram Equalization | UNet | Dice Coefficient = 0.876 |
| J. Sun et al. [48] | 173 | Adaptive Median Filter and Histogram Equalization | FCN-AlexNet | mIoU = 0.81 |
| M. Buda et al. (2019) [49] | 1278 | Contrast stretching | U-Net | Dice Coefficient = 0.93 |
| Zihao Guo et al. (2020)[50] | 1400 | Histogram Equalization | DeepLabv3 + | Dice Coefficient = 0.94 |
| Gomes Ataide et al. (2021)[51] | 6066 | Resizing and cropping | ResUNet | mDC—0.857 & mIoU—0.767 |
| Proposed method (Hybrid_UNet) | 820 | DsF_EPSF | Hybrid-UNet | mIoU—86.6 ± 9.8 and mDC = 93.2 ± 3.1 |