Machine learning algorithms for classifying corneas by Zernike descriptors

Abstract

Keratoconus is the most common primary ectasia, as the treatment is not easy, its early diagnosis is essential. The main goal of this study is to develop a method for classification of specific types of corneal shapes where 55 Zernike coefficients (angular index m = 9) are used as inputs. We describe and apply six Machine Learning (ML) classification methods and an ensemble of them to objectively discriminate between keratoconic and non-keratoconic corneal shapes. Earlier attempts by other authors have successfully implemented several Machine Learning models using different parameters (usually, indirect measurements) and have obtained positive results. Given the importance and ubiquity of Zernike polynomials in the eye care community, our proposal should be a suitable choice to incorporate to current methods which might serve as a prescreening test. In this project we work with 475 corneas, classified by experts in two groups, 50 keratoconics and 425 non-keratoconics. All six models yield high rated results with accuracies above 98%, precisions above 97%, or sensitivities above 93%. Also, by building an assembly with the models, we further improve the accuracy of our classification, for example we found an accuracy of 99.7%, a precision of 99.8% and sensitivity of 98.3%. The model can be easily implemented in any system, being very simple to use, thus providing ophthalmologists with a effortless and powerful tool to make a first diagnosis.