Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning. (March 2022)

• Record Type: Journal Article
• Authors: Khan, Sibghatullah I.; Choubey, Shruti Bhargava; Choubey, Abhishek; Bhatt, Abhishek; Naishadhkumar, Pandya Vyomal; Basha, Mohammed Mahaboob
• Is Part Of: Concurrent engineering, research and applications. Volume 30:Number 1(2022)
• Page Start: 103
• Page End: 115
• Publication Date: 2022-03
• Subjects: Glaucoma fundus images -- wavelet transform -- feature extraction -- feature selection -- LS-SVM; Production engineering -- Periodicals; Concurrent engineering -- Periodicals; 621.39
• DOI: 10.1177/1063293X211026620
• Languages: English
• ISSNs: 1063-293X

Abstract

Glaucoma is a domineering and irretrievable neurodegenerative eye disease produced by the optical nerve head owed to extended intra-ocular stress inside the eye. Recognition of glaucoma is an essential job for ophthalmologists. In this paper, we propose a methodology to classify fundus images into normal and glaucoma categories. The proposed approach makes use of image denoising of digital fundus images by utilizing a non-Gaussian bivariate probability distribution function to model the statistics of wavelet coefficients of glaucoma images. The traditional image features were extracted followed by the popular feature selection algorithm. The selected features are then fed to the least square support vector machine classifier employing various kernel functions. The comparison result shows that the proposed approach offers maximum classification accuracy of nearly 91.22% over the existing best approaches.