Table 1.
Machine learning techniques applied for keratoconus classification in the literature.
| Authors | Classification | Network | Accuracy | Sensitivity | Specificity |
|---|---|---|---|---|---|
| Accardo et al. | Normal and keratoconus | BPN | 98 | 93.3 | 98.6 |
| Souza et al. | Normal and keratoconus | SVM | — | 100 | 75 |
| MLP | — | 100 | 75 | ||
| RBFNN | — | 98 | 75 | ||
| Toutounchian et al. | Normal, mild keratoconus, and keratoconus | MLP | 77.6 | — | — |
| SVM | 72 | — | — | ||
| DT | 84 | — | — | ||
| RBFNN | 71.2 | — | — | ||
| Arbelaez et al. | Normal, abnormal, subclinical, and keratoconus | SVM | 95.275 | 87.6 | 96.9 |
| Hidalgo et al. | Astigmatism, forme fruste keratoconus, keratoconus, normal, and postrefractive surgery | SVM | 88.8 | 77.22 | 97.02 |
| Lavric et al. | Keratoconus, forme fruste keratoconus, and normal | QSVM | 93 | — | — |
| Santos et al. | Normal and keratoconus | CorneaNet | 99.56 | ||
| Kamiya et al. | Normal and keratoconus 4 gradings | ResNet-18 | 99.1 | 100 | 98.4 |
| Shi et al. | Normal, keratoconus, and subclinical | NN | 93 | ||
| Kuo et al. | Normal and keratoconus | VGG16 | 93.1 | 91.7 | 94.4 |
| InceptionV3 | 93.1 | 91.7 | 94.4 | ||
| ResNet152 | 95.8 | 94.4 | 97.2 | ||
| Lavric et al. | 5 classes as normal, forme fruste, keratoconus II, keratoconus III, and keratoconus IV | SVM | AUC 0.88 | ||
| 3 classes as normal, forme fruste, and keratoconus | SVM | AUC 0.96 | |||
| Normal and keratoconus | SVM | AUC 0.99 | |||
| Cao et al. | Normal and keratoconus | SVM | 88.8 |