Summary of previous research with the best method and result.
| Research | Subject | Method | Result |
|---|---|---|---|
| [2] | Determining the base curve by manual and device-based methods | Calculens algorithm | Success rate 50.6% |
| [6] | Determining the base curve based on CAE features | Basic CAE and pre-trained CAE on the features of Pentacam images | MSE = 0.056 |
| [4] | Image processing/machine learning-based RGP lens base-curve determination on Pentacam four refractive maps | Classical image processing method with combinatory feature fusion | MSE = 0.053 |
| [5] | Deep learning-based CCA fusion determining RGP lens base-curve on Pentacam four refractive maps | Deep learning feature extraction and CCA feature fusion | MSE = 0.056 |
| [7] | Deep learning-based autoencoder fusion determining RGP lens base-curve on Pentacam four refractive maps | Deep learning feature extraction and CAE feature fusion | MSE = 0.014R2 = 0.53 |
| [33] | Novel deep learning approach to estimate RGP contact lens base-curve for keratoconus fitting | Using the U-net architecture | MSE = 0.05 |
| [32] | Classification of Pentacam images into four categories: type I to III keratoconus and natural eyes | Integrate multiple classifiers as an expert system and a gateway to combine the results of categories (each category is trained with a specific piece of training data) | Accuracy = 92% |
| [12] | Categorization of Pentacam images is categorized into three classes: keratoconus, subclinical keratoconus, and normal cornea with Pentacam images | Pre-processing of images and a convolutional neural network | Accuracy = 98% |
CAE: convolutional autoencoder; CCA: canonical correlation analysis; RGP: rigid gas permeable.