DOI: 10.14704/NQ.2022.20.11.NQ66345

MONITORING OF GLAUCOMA IN REAL TIMEIMAGES OF THE FUNDUS OBTAINED WITH OD

Dr. A. Suresh Rao1, Dr.M. Narender1

Abstract

Damage to the optical nerve, which carries visual signals to the brain, is the direct cause of the irreversible vision loss that results from glaucoma. Since glaucoma advances without warning and cannot be reversed in its later stages, early detection is essential. Although several deep learning models have been addressed to the problem of diagnosing glaucoma from digital fundus pictures, their generalisation performance has been limited by a lack of labelled data, in addition to their high computational cost and unique hardware requirements. Here, we suggest using We test the efficacy of three different compact Self-Organizing Operational Neural Network OD architectures for early glaucoma detection in fundus images vs more conventional (deep) Convolutional Neural Networks (CNNs) (ACRIMA, RIM-ONE, and ESOGU). The results of the experiments demonstrate that OD is a viable network model for biomedical datasets, especially in scenarios with little data, since it not only provides outstanding detection performance but also has the potential to significantly reduce the computational complexity.

Keywords

Glaucoma,Diabetic Retinopathy, Convolutional Neural Networks.

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