Research Article
Glaucoma Stage Classification Using Image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus Images
@INPROCEEDINGS{10.1007/978-3-031-48888-7_33, author={D. Shankar and I. Sri Harsha and P. Shyamala Madhuri and J. N. S. S. Janardhana Naidu and P. Krishna Madhuri and Srikanth Cherukuvada}, title={Glaucoma Stage Classification Using Image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus Images}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={glaucoma intraocular pressure retina computer-aided diagnosis image decomposition fundus images optic nerve head deep learning glaucoma classification optic disc ensemble classifiers RFCN RCNN}, doi={10.1007/978-3-031-48888-7_33} }- D. Shankar
I. Sri Harsha
P. Shyamala Madhuri
J. N. S. S. Janardhana Naidu
P. Krishna Madhuri
Srikanth Cherukuvada
Year: 2024
Glaucoma Stage Classification Using Image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus Images
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_33
Abstract
Glaucoma is an ocular pathology characterized by the gradual deterioration of neural cells in the eye, which is attributed to elevated intra ocular pressure within the retina. Glaucoma takes the second spot in terms of its prevalence as a neurodegenerative eye disease, failure to diagnose glaucoma at an early stage can lead to complete blindness. This underlying issue requires a streamlined system that uses experienced medical experts, little equipment, and less time. To categories the stages of glaucoma, a Computer-Aided Diagnosis (CAD) method is used called Image Empirical Mode Decomposition (IEMD). Segmentation-based algorithms utilizing features like cup-to-disc ratio (CDR) and textural characteristics close to the optic disc region can distinguish glaucoma. To capture pixel variations for this investigation, the pre-processed fundus images are separated and transformed into a diverse range of intrinsic mode function (IMFs). The study employs deep learning-based framework for finding the optic nerve head from coloured fundus photographs, to transform the fundus images into a delimited region of interest (ROI) and utilizes multiple deep networks like RFCN and RCNN classifiers identified glaucoma stages separately. The CAD system serves as an automated tool for retinal image processing and demonstrates superior performance over the RFCN classifier, achieving an impressive accuracy of 96% with the RCNN classifier in classifying glaucoma stages.
