Fig. 1.
Proposed DEEP-BTS methodology.
Fig. 2.
Pre-processing step in the proposed method.
Fig. 3.
Architecture of ResU-Net.
Fig. 4.
Experimental results of the proposed DEEP-BTS.
Table 1.
Performance evaluation of the DEEP-BTS.
| Types | AC | SP | RE | PR | F1 |
|---|---|---|---|---|---|
| CSF | 99.12 | 97.91 | 97.43 | 98.76 | 97.65 |
| GM | 98.36 | 96.75 | 98.14 | 97.13 | 96.14 |
| WM | 99.25 | 98.58 | 96.87 | 98.83 | 95.76 |
| Overall | 98.91 | 97.74 | 97.48 | 98.24 | 96.51 |
Fig. 5.
(a) Accuracy and (b) Loss curve of the ResU-Net.
Fig. 6.
Comparison of the existing segmentation technique with ResU-Net.
Fig. 7.
Segmentation comparison of standard U-Net and the proposed ResU-Net.