| Approach | Study or MC-MLDCNN with different parameters | Accuracy | Precision | Recall | score | FPR |
| MC-MLDCNN with different parameters | Model 1 (, , , ) | 99.12 | 99.39 | 98.46 | 98.93 | 0.42 | Model 2 (, , , , ) | 99.29 | 99.39 | 98.88 | 99.13 | 0.42 | Model 3 (, , , ) | 99.34 | 99.76 | 98.63 | 99.19 | 0.16 | Model 4 (, , , ) | 99.00 | 99.00 | 98.56 | 98.78 | 0.69 | Model 5 (, , , , ) | 99.34 | 99.62 | 98.75 | 99.19 | 0.26 | Model 6 (, , , , ) | 99.08 | 99.46 | 98.30 | 98.88 | 0.37 | Proposed (, , , ) | 99.36 | 99.65 | 98.80 | 99.22 | 0.24 |
| Competitive deep learning models in the literature | Hao et al. [36] | 98.35 | 99.00 | 98.17 | 98.58 | 1.40 | Jemal et al. [2] | 99.25 | 97.73 | 99.35 | 98.53 | — | Gong et al. [19] | 97.79 | 98.54 | 96.04 | 97.27 | — | Odumuyiwa and Chibueze [45] | 96.39 | 98.83 | 95.0 | 96.88 | 2.00 | Rizvi et al. [52] | 96.85 | 97.62 | 94.64 | 96.11 | 1.60 |
| Classic deep learning models | CNN | 97.93 | 97.96 | 96.97 | 97.46 | 1.40 | LSTM | 97.70 | 97.91 | 96.46 | 97.18 | 1.43 | Bi-LSTM | 97.54 | 97.98 | 96.00 | 96.98 | 1.38 |
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