Texture Based Feature Extraction and Classification of Retinal Fundus Image for Glaucoma Detection

Abstract

Glaucoma is the most common reason for permanent blindness and disability in the universe. Despite this, the vast majority of people are unaware that they have the infection, and detecting glaucoma progression with current technology continues to be a challenge in clinical research. The implementation of a machine-learning algorithm to the retinal fundus image gives a solution. In this research, 250 glaucomatous and 250 healthy retinal fundus images from the ACRIMA. For cleaning the data some pre-processing techniques are used. The feature extraction techniques such as Gray-Level Co-occurrence Matrix (GLCM), Gray Level Run-Length Matrix (GLRM), and Histogram of Oriented Gradients (HOG) are applied to the clean data. This process helps to retrieve beneficial information from the data. Finally, the retrieved features are taken as input to the machine learning classifier such as Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor (K-NN), and Linear Discriminant Analysis (LDA) to construct the model. For assessment of feature extraction and classification, several performance metrics like accuracy, True and False Positive Rate (TPR/FPR), True and False Negative Rate (TNR/FNR), precision, and Performance Index (PI) are used. From the analysis, it is found that the GLCM feature extraction with the Naïve Bayes model is the finest combination and it gives a 95% accuracy level.