Histogram of Oriented Gradients (HOG)-Based Artificial Neural Network (ANN) Classifier for Glaucoma Detection

Abstract

Glaucoma is a severe condition of the optic nerve resulting in the loss of eyesight. The proposed methodology has introduced the extraction of HOG (histogram of oriented gradients) features from the retinal fundus image. After the removal of HOG features, the authors compare the performance of five different machine learning techniques like k-nearest neighbour (KNN), support vector machine (SVM), linear discriminant analysis (LDA), naïve bayes, and artificial neural network. The process of image classification is based on analyzing the numerical properties of the obtained image features and classifying the data into different categories. In the paper, the authors intend to classify whether the image belongs to the glaucomatous category or the healthy category. After the application of the different classification algorithms to the test data and further analysis of the results, they could conclude that the SVM classifier provided an accuracy of 90%, KNN 86%, Naïve Bayes 96%, LDA 86%, and ANN 96.90% on the dataset in hand.