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What is Automated Glaucoma Diagnosis via WPD about?

This document proposes a technique for automated glaucoma screening from fundus retinal images using wavelet packet decomposition. It explores using both approximate and detailed coefficients from WPD. Features of energy and entropy are extracted from 84 sub-bands and used to classify images with and without considering regions of interest. Classification accuracy is improved from 79% without ROIs to 85% when considering biologically significant ROIs, highlighting the importance of detailed sub-band feature

Author

Raja Chandru

Language

EN