Glaucoma Detection using Fundus Image of the Retina

Glaucoma is often regarded as the primary cause of irreversible visual loss. Early glaucoma diagnosis is essential for effective treatment and the preservation of vision. Identifying Glaucoma Using Image Processing Techniques the main effect that glaucoma has on the optic disc is an enlargement of the eye socket. The second most prevalent cause of blindness is glaucoma, which has historically been challenging to identify in its early stages. Glaucoma is the most common cause of permanent blindness worldwide. Therefore, early detection is crucial for the prevention and proper treatment of vision loss. The development of computer-aided glaucoma diagnostic tools has improved significantly in recent years with the use of convolution neural networks (CNNs). This study offers an overview of contemporary CNN-based glaucoma diagnosis algorithms and concentrates on investigations completed up to 2021. To understand when irregularities occur, preprocessing techniques such as filtering, green channel extraction, and CLAHE are applied. The suggested classifier examines these images to determine if glaucoma is present or not computationally. CDR of the desired image. Compare the accuracy of the proposed classifier with that of rival methods. They use soft computing methods and hybrid algorithms for morphology-based image classification.