Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning
| Autor: | Abhishek Bhatt, Pandya Vyomal Naishadhkumar, Shruti Bhargava Choubey, Abhishek Choubey, M. Mahaboob Basha, Sibghatullah I. Khan |
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| Rok vydání: | 2021 |
| Předmět: |
genetic structures
business.industry Computer science Noise reduction Eye disease Feature extraction General Engineering Glaucoma Wavelet transform 020207 software engineering 02 engineering and technology Fundus (eye) medicine.disease eye diseases Computer Science Applications Wavelet Modeling and Simulation 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Computer vision sense organs Artificial intelligence business |
| Zdroj: | Concurrent Engineering. 30:103-115 |
| ISSN: | 1531-2003 1063-293X |
| DOI: | 10.1177/1063293x211026620 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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