Table 5.
Numerical investigation between the existing and the proposed Bi-LSTM-based modified genetic algorithm.
| Models | Sensitivity (%) | Accuracy (%) | Specificity (%) |
|---|---|---|---|
| ResNeXt-101 with Bi-LSTM (Burduja et al., 2020) | 72.86 | 97.83 | 99 |
| 2D CNN (Wang et al., 2021) | 95.84 | 95 | 94.85 |
| Synergistic deep learning model (Anupama et al., 2022) | 94.01 | 95.73 | 97.78 |
| OGRU-CSA (Sengupta and Alzbutas, 2022) | 99.25 | 99.36 | 99.40 |
| Parallel deep convolutional model with boosting mechanism (Asif et al., 2023) | 96.50 | 97.70 | – |
| Bi-LSTM-based modified genetic algorithm | 99.40 | 99.80 | 99.48 |