Keratoconus is a chronic-degenerative disease which results in progressive corneal thinning and steepening leading to irregular astigmatism and decreased visual acuity that in severe cases may cause debilitating visual impairment. In recent years, different Machine Learning methods have been applied to distinguish either normal and keratoconic eyes. These methods utilize both corneal curvature maps and their corresponding numeric indices to perform the classification. The main objective of this study is to evaluate the performance of features extracted with Histograms of Oriented Gradients (HOG) and with Convolutional Neural Networks (CNN) in the classification of normal and keratoconic eyes, using axial map of the anterior corneal surface. Two distinct models were trained using the same Multilayer Perceptron (MLP) architecture: one of them using the HOG features as input, and the other with the CNN features. The Topographic Keratoconus Classification index (TKC) provided by Pentacam™ was used as a label and the KC2-labeled maps were defined as keratoconus. Each model was trained using 3,000 images of normal and 3,000 keratoconic eyes, and then validated and tested on 1,000 images of each label. The model trained with HOG features exhibited a sensitivity of 99.1% and specificity of 98.7%, with an Area Under the Curve (AUC) of 0.999143. The model trained with CNN features showed both sensitivity and specificity of 99.5%, and AUC = 0.999778. The results suggest that the performance of the classifier is similar for both types of features.
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