Comparison between histogram of oriented gradients and convolutional features for keratoconus detection using corneal curvature maps

Lucas Orlandi de Oliveira; Felipe Marques de Carvalho Taguchi; Renato Feijó Evangelista; André Orlandi de Oliveira; Edson Shizuo Mori; Jarbas Caiado de Castro Neto; Wallace Chamon

doi:10.1117/12.2677521

4 October 2023 Comparison between histogram of oriented gradients and convolutional features for keratoconus detection using corneal curvature maps

Lucas Orlandi de Oliveira, Felipe Marques de Carvalho Taguchi, Renato Feijó Evangelista, André Orlandi de Oliveira, Edson Shizuo Mori, Jarbas Caiado de Castro Neto, Wallace Chamon

Author Affiliations +

Proceedings Volume 12675, Applications of Machine Learning 2023; 1267513 (2023) https://doi.org/10.1117/12.2677521
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Lucas Orlandi de Oliveira, Felipe Marques de Carvalho Taguchi, Renato Feijó Evangelista, André Orlandi de Oliveira, Edson Shizuo Mori, Jarbas Caiado de Castro Neto, and Wallace Chamon "Comparison between histogram of oriented gradients and convolutional features for keratoconus detection using corneal curvature maps", Proc. SPIE 12675, Applications of Machine Learning 2023, 1267513 (4 October 2023); https://doi.org/10.1117/12.2677521

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