Eye Disease Glaucoma - Detection Using Hybrid Classification Model Using Machine Learning Principles

Glaucoma is a neurological disease and one of the most well-known causes of vision loss, according to the abstract. Because nerve degeneration is an irreversible process, early detection of the condition is essential to prevent permanent visual loss. Glaucoma is mostly caused by elevated intraocular pressure, and if it is not identified and treated promptly, it can damage vision. Glaucoma is a vision condition that gradually becomes worse over time and affects the eye's optic nerve. It results from pressure accumulation inside the eye. Glaucoma frequently runs in families and may not manifest itself until later in life. One of the most crucial and difficult parts is the identification of glaucomatous progression. In this study, we presented a unique hybrid algorithm for glaucoma diagnosis. In this study, we provide a novel hybrid approach for classification utilizing Artificial Neural Networks (ANN) and support vector machines (SVM). For segmentation, we used HMM with Cuckoo search optimization (CSO), and for classification, we employed a hybrid of SVM and ANN. When compared to other approaches already in use, the results demonstrate strong performance.