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

Author:

Singh Law Kumar1ORCID,Pooja 2,Garg Hitendra3,Khanna Munish4

Affiliation:

1. Sharda University, India; Hindustan College of Science and Technology, India

2. Sharda University, India

3. GLA University, India

4. Hindustan College of Science and Technology, India

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.

Publisher

IGI Global

Subject

Artificial Intelligence,Computational Theory and Mathematics,Computer Science Applications

Reference29 articles.

1. Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features

2. Detection of glaucoma using retinal fundus images

3. Ali, M. A., Hurtut, T., Faucon, T., & Cheriet, F. (2014, March). Glaucoma detection based on local binary patterns in fundus photographs. In Medical Imaging 2014: Computer-Aided Diagnosis (Vol. 9035, p. 903531). International Society for Optics and Photonics.

4. Clinical Evaluation of the Proper Orthogonal Decomposition Framework for Detecting Glaucomatous Changes in Human Subjects

5. A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

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