From: Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks
Classification techniques | Accuracy | Specificity | Sensitivity | F-score |
---|---|---|---|---|
MLP | 0.85 ± 0.33 | 0.83 ± 0.26 | 0.87 ± 0.22 | 0.84 ± 0.30 |
RNN | 0.87 ± 0.23 | 0.88 ± 0.31 | 0.85 ± 0.21 | 0.84 ± 0.29 |
RBFNN | 0.79 ± 0.22 | 0.75 ± 0.23 | 0.74 ± 0.34 | 0.74 ± 0.21 |
ELM | 0.90 ± 0.15 | 0.87 ± 0.32 | 0.91 ± 0.22 | 0.89 ± 0.25 |
PNN | 0.89 ± 0.18 | 0.90 ± 0.28 | 0.87 ± 0.29 | 0.88 ± 0.32 |
TDNN | 0.86 ± 0.32 | 0.85 ± 0.25 | 0.88 ± 0.23 | 0.86 ± 0.29 |
DWT-PCA-DBN | 0.95 ± 0.14 | 0.94 ± 0.16 | 0.97 ± 0.26 | 0.95 ± 0.15 |