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.

中文翻译：

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用于青光眼检测的混合特征和优化驱动的递归神经网络

青光眼被认为是不可逆转的视力丧失的主要来源。青光眼的早期诊断对于提供早期治疗和减少视力丧失至关重要。眼底图像在眼科中变形并用于可视化视盘的结构。然而，准确性被认为是一个主要的限制因素。为了提高准确性，开发了一种有效的优化驱动分类器用于青光眼检测。提出的 Jaya-chicken swarm 优化 (Jaya-CSO) 用于训练循环神经网络 (RNN) 以进行青光眼检测。所提出的 Jaya-CSO 是通过将 Jaya 算法与鸡群优化 (CSO) 技术相结合而设计的，用于调整 RNN 分类器的权重。该方法利用了视盘特征、统计特征、和血管特征，用于确定青光眼区域。从视盘、血管和眼底图像获得的特征被公式化为特征向量。最后，使用特征向量使用 RNN 完成青光眼分类，以便使用提议的 Jaya-CSO 训练 RNN。提出的 Jaya-CSO 分别以 0.97 的最大准确度、0.97 的特异性和 0.97 的灵敏度优于其他现有模型。