Table 2.
Comparison of classification performance with existing research results
| References | Data | Method | Accuracy | Sensitivity | Specificity |
|---|---|---|---|---|---|
| Gonuguntla et al.49. | 65AD + 65CN | ROIs | 85.3% | - | - |
| Kang et al.20. | 187AD + 229CN | DCGAN | 84.23% | - | - |
| Vgg16 | 83.57% | - | - | ||
| ResNet50 | 74.7% | - | - | ||
| Kushol et al.50. | 159AD + 229CN | Transformer | 88.2% | 95.6% | 77.4% |
| Cai et al.51. | 49AD + 43CN | Graph Transformer | 85.9% | 79.8% | 92.8% |
| Lu et al.52. | 193AD + 345CN | Vision Transformer | 91.77% | 84.38% | - |
| Qiu et al.53. | 188AD + 229CN | FCN | 83.4% | 76.7% | 88.9% |
| Shahamat et al.54. | 70AD + 70CN | 3D CNN | 85% | - | - |
| Shojaei et al.55. | 74AD + 71CN | 3D CNN | 87% | - | - |
| Tinauer et al.56. | 128AD + 290CN | 3D CNN | 86.19% | 79.73% | 92.66% |
| ours | 110AD + 176CN | 3D CNN | 93.1% | 81.82% | 100% |