Title: Performance analysis of machine learning techniques for glaucoma detection based on textural and intensity features

Authors: Law Kumar Singh; Hitendra Garg; Pooja; Munish Khanna

Addresses: Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Knowledge Park III, Greater Noida, India; Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, India ' Department of Computer Engineering and Applications, GLA University, Mathura, India ' Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Knowledge Park III, Greater Noida, India ' Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, India

Abstract: Glaucoma is one of the significant causes of blindness, which covers about 15% to 20% of the total population, so early-stage detection is essential. The proposed methods apply fast fuzzy C-means approach to determine optics-cup-to-disc ratio (CDR) followed by textural based and intensity-based features of the eye. Textural features include local binary pattern, grey-level co-occurrence matrix, and Harlick features, whereas intensity-based features include colour moment and skewness. Machine learning techniques are applied to extract entropy, horizontal, vertical diameter of optics disc/cup, textural based and intensity-based features that classify the image as glaucoma or healthy image and obtained ophthalmologists verify results. Own dataset of 298 retinal images consisting of both healthy and glaucomatous images is used for experimental analysis. In the proposed method, various machine learning techniques like support vector machine (SVM), K-nearest neighbour, and naive Bayes, report 95.5%, 93.3%, and 94.35% accuracy, respectively.

Keywords: glaucoma disease; retinal fundus image; CDR; K-nearest neighbour; K-NN; naive Bayes; support vector machine; SVM; fast fuzzy C-mean; local binary pattern; Harlick features; grey level co-occurrence matrix; optic disc; textural features; intensity features.

DOI: 10.1504/IJICA.2020.111230

International Journal of Innovative Computing and Applications, 2020 Vol.11 No.4, pp.216 - 230

Received: 05 Jan 2020
Accepted: 12 Mar 2020
Published online: 16 Nov 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article