Table 14.
Comparison of the proposed and state-of-the-art methods.
| Method | Experimental protocol (train/test ratio in %) |
Accuracy (%) |
|---|---|---|
| ResNet-18 (Tang and Teoh, 2023) | 80/20 | 88.33 |
| CLM-DL (Woźniak et al., 2023) | 80/20 | 95.09 |
| GNN (Gürsoy and Kaya, 2024) | 80/20 | 93.68 |
| FL-efficientNetB0 (Zhou et al., 2024) | 80/20 | 80.17 |
| SE-denseNet-121 (Mao et al., 2025) | 85/15 | 95.60 |
| Customized CNN (Dewage et al., 2024) | 85/15 | 94.51 |
| CFLM-Grad-CAM (Latif et al., 2025) | — | 94.24 |
| 3D-CNN-RF (Yin and Wang, 2025) | 80/20 | 95.00 |
| CNN-SVM (Suryawanshi and Patil, 2024) | 80/20 | 95.16 |
| VGG16-ANN (Castelino et al., 2024) | 80/20 | 84.00 |
| EWHO-SqueezeNet (Kanchanamala et al., 2025) | 5-FCV | 93.20 |
| GTLO-LeNet (Aluri and Imambi, 2024) | 80/20 | 89.60 |
| SCAOA-SegNet (Geetha et al., 2024) | 80/20 | 93.00 |
| Proposed method (CNN-KNN) |
5-FCV | 96.10 |
| 80/20 | 96.58 |