In International ophthalmology
PURPOSE : The present study was done to evaluate efficiency of an ensemble learning structure for automatic keratoconus diagnosis and to categorize eyes into four different groups based on a combination of 19 parameters obtained from Pentacam measurements.
METHODS : Pentacam data from 450 eyes were enrolled in the study. Eyes were separated into training, validation, and testing sets. An ensemble system was used to analyze corneal measurements and categorize the eyes into four groups. The ensemble system was trained to consider indices from both anterior and posterior corneal surfaces. Efficiency of the ensemble system was evaluated and compared in each group.
RESULTS : The best accuracy was achieved by the ensemble system with both multilayer perceptron and neuro-fuzzy system classifiers alongside the Naïve Bayes combination method. The accuracy achieved in KC versus N distinction task was equal to 98.2% with 99.1% of sensitivity and 96.2% of specificity for KC detection. The global accuracy was equal to 98.2% for classification of 4 groups, with an average sensitivity of 98.5% and specificity of 99.4%.
CONCLUSION : In this study, authority of an ensemble learning system to work out intricate problems was presented. Despite using fewer parameters, herein, comparable or, in some cases, better results were obtained than methods reported in the literature. The proposed method demonstrated very good accuracy in discriminating between normal eyes and different stages of keratoconus eyes. In some cases, it was not possible to directly compare our results with the literature, due to differences in definitions of KC group as well as differences in selection of items and parameters.
Ghaderi Maryam, Sharifi Arash, Jafarzadeh Pour Ebrahim
Artificial neural network, Ensemble learning, Keratoconus, Machine learning, Neuro-fuzzy, Pentacam