Table 6 Past work on the diagnosis of human burns using various ML and DL.
From: Spatial attention-based residual network for human burn identification and classification
Study | Model | Classification | Dataset Size | Accuracy (%) |
|---|---|---|---|---|
Abubakar et al.15 | Pre-trained ResNet50 | Binary Class | 2080 images | 95.43 |
| Â | Pre-trained VGG16 | Â | Â | 85.63 |
Smith et al.16 | Pre-trained ResNet50 | Binary Class | 1360 images (Caucasians dataset) | 99.3 |
| Â | Â | Â | 540 images (African dataset) | 97.1 |
Ugail et al.44 | ResNet101 | Binary Class | 1360 images | 95.9 |
Buhar et al.17 | VGG-Face | Binary Class | 1420 images | 95.208 |
Yadav et al.40 | SVM | Binary Class | 94 segmented images | 82.43 |
Shin et al.45 | SSL | Multiclass | 170 images | 70.0 |
Rahman et al.46 | SVM | Multiclass | 500 images | 93.3 |
