Glaucoma detection using hybrid architecture based on optimal deep neuro fuzzy network

What is it about?

Glaucoma represents dangerous ailment, which affected the nervous model and causes loss of vision. Several researchers developed automated discovery of glaucoma, but redundancy elimination is still challenging. Hence, this research study introduces an effective method for detecting glaucoma with deep neurofuzzy network (DNFN). Initially, the retinal image is input for preprocessing to remove the noises. Then, the optic disc (OD) detection and blood vessel segmentation are employed using the blackhole entropy fuzzy clustering algorithm and the DeepJoint model, respectively. Finally, the obtained OD and blood vessels are fed to the DNFN, wherein DNFN training is performed with newly devised MultiVerse Rider Wave Optimization (MVRWO). The newly developed MVRWO integrates the Water Wave Optimization, Rider Optimization Algorithm, and MultiVerse Optimizer. Finally, the output is classified based on the loss function of the DNFN. The developed MVRWO–DNFN obtained an elevated accuracy of 92.214%, a sensitivity of 93.422%, and a specificity of 92.34%

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Why is it important?

Glaucoma is a serious eye disease that affects the optic nerve and can lead to vision loss if left untreated. Early detection and intervention are crucial for preventing vision impairment and blindness associated with glaucoma. While several researchers have worked on automated methods for detecting glaucoma, challenges such as redundancy elimination and accurate detection still persist. Addressing these challenges is essential for improving the effectiveness of automated glaucoma detection systems.

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