Keratoconus is a chronic-degenerative disease which results in progressive corneal thinning and steeping leading to irregular astigmatism and decreased visual acuity that in severe cases may cause debilitating visual impairment. In recent years, Machine Learning methods, especially Convolutional Neural Networks (CNN), have been applied to classify images according to either presence or absence of the disease, based on different corneal maps. This study aims to develop a novel CNN architecture to classify axial curvature maps of the anterior corneal surface in five different grades of disease (i: normal eye; ii: suspect eye; iii: subclinical keratoconus; iv: keratoconus; and v: severe keratoconus). The dataset comprises 3, 832 axial curvature maps represented on relative scale and labeled by ophthalmologists. The images were splitted into three distinct subsets: training (2, 297 images ≈ 60%), validation (771 images ≈ 20%), and test (764 images ≈ 20%) sets. The model achieved an overall accuracy of 78.53%, a macro-average sensitivity of 74.53% (87.50% for normal eyes, 46.56% for suspect eyes, 65.41% for subclinical keratoconus, 93.42% for keratoconus, and 79.25% for severe keratoconus) and a macro-average specificity of 94.42% (92.14% for normal eyes, 95.30% for suspect eyes, 93.82% for subclinical keratoconus, 91.24% for keratoconus, and 99.58% for severe keratoconus). Additionally, the model achieved AUC scores of 0.97, 0.92, 0.90, 0.98, and 0.94 for normal eye, suspect eye, subclinical keratoconus, keratoconus, and severe keratoconus, respectively. The results suggest that the CNN exhibited notable proficiency in distinguishing between normal eyes and various stages of keratoconus, offering potential for enhanced diagnostic accuracy in ocular health assessment.
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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