Hybrid features and optimization‐driven recurrent neural network for glaucoma detection

Abstract

Glaucoma is considered as the main source of irrevocable loss of vision. The earlier diagnosis of glaucoma is essential to provide earlier treatment and to reduce vision loss. The fundus images are transfigured in the ophthalmology and are used to visualize the structures of the optic disc. However, accuracy is considered as a major constraint. To increase accuracy, an effective optimization‐driven classifier is developed for glaucoma detection. The proposed Jaya‐chicken swarm optimization (Jaya‐CSO) is employed for training the recurrent neural network (RNN) for glaucoma detection. The proposed Jaya‐CSO is designed by integrating the Jaya algorithm with the chicken swarm optimization (CSO) technique for tuning the weights of the RNN classifier. The method utilized optic disc features, statistical features, and blood vessel features for the determination of the glaucomatous region. The features obtained from the optic disc, blood vessels, and the fundus image is formulated as a feature vector. Finally, the glaucoma classification is done using RNN using the feature vector such that the RNN is trained using the proposed Jaya‐CSO. The proposed Jaya‐CSO outperformed other existing models with maximal accuracy of 0.97, the specificity of 0.97, and sensitivity of 0.97, respectively.

Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images

“…Superpixels are grouped from adjacent pixels to detect glaucoma damage; machine learning classifiers analysed the proposed algorithm that uses SD-OCT images. In the recently published study on glaucoma, the authors (Ajesh et al [ 2 ]) aimed to boost the sensitivity of glaucoma diagnosis through a novel classification focused on efficient optimisation. The Jaya-chicken swarm optimization (Jaya-CSO) proposal combines the Jaya algorithm with the CSO system to change the RNN classifier weights.…”

A novel hybrid robust architecture for automatic screening of glaucoma using fundus photos, built on feature selection and machine learning‐nature driven computing

“…In a recent report, researchers (Shehryar et al, 2020) used both a fundus examination (binocular ophthalmoscopy) and an extremely sensitive OCT scanning system for the eye that explored the back of the eye to determine the hard and soft tissues of the eye. The researchers (Ajesh & Ravi, 2020) used artificial intelligence and machine learning to create a successful approach for glaucoma research. The proposed Jaya‐chicken swarm optimization (CSO) was designed to incorporate into the recurrent neural network (RNN) classifier two techniques: the Jaya algorithm, in which the topology of the RNN was created, and the CSO, in which a group of virtual CogNets were trained together to boost their weights in the RNN.…”

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

“…This method attained better accuracy, but the computation time was high. Ajesh and Ravi 29 implemented Jaya‐chicken swarm optimization (Jaya‐CSO) for training the RNN for effective detection of glaucoma. Here, the glaucomatous region was determined using different features for enhancing the accuracy.…”