Table 6 Comparative results of the EBTS-BIFMFBO method with existing approaches. Evaluation is based on key metrics to highlight improvements in segmentation accuracy and efficiency37,38,39,40.
Technique | \(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\) | \(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\) | \(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\) | \(\:{\varvec{F}1}_{\varvec{s}\varvec{c}\varvec{o}\varvec{r}\varvec{e}}\) |
|---|---|---|---|---|
Deep-CNN Model | 90.94 | 95.72 | 96.84 | 93.17 |
DRLBP + CNN | 93.27 | 95.64 | 90.47 | 92.85 |
DenseNet- 161 | 90.56 | 95.37 | 91.74 | 96.87 |
DCNN-SGD | 99.08 | 91.91 | 92.65 | 96.93 |
GoogleNet | 94.06 | 92.76 | 92.50 | 94.60 |
3D ConvNet | 97.07 | 90.21 | 94.42 | 96.04 |
VGG Net Method | 97.18 | 90.00 | 93.52 | 92.60 |
EBTS-BIFMFBO | 99.16 | 98.68 | 98.72 | 98.70 |
