Fundus image classification methods for the detection of glaucoma: A review

Area of Science:

• Ophthalmology
• Neuroscience
• Medical Imaging

Background:

• Glaucoma is a neurodegenerative disease and a primary cause of irreversible vision impairment.
• Elevated intraocular pressure is a key factor, often leading to optic nerve degeneration.
• Late-stage diagnosis is common due to subtle early symptoms, necessitating effective detection methods.

Purpose of the Study:

• To provide an overview of glaucoma, including its symptoms and risk factors.
• To review various classification and segmentation methodologies for glaucoma identification and diagnosis.
• To evaluate current research on glaucoma findings and treatment options.

Main Methods:

• Review of existing literature on glaucoma diagnosis.
• Analysis of classification techniques for disease identification.
• Evaluation of image segmentation approaches for detecting glaucomatous changes.
• Assessment of research related to glaucoma findings and treatments.

Main Results:

• Glaucoma diagnosis relies on assessing optic nerve damage and visual function.
• Various classification and segmentation methods show promise for early glaucoma detection.
• The article highlights the importance of timely diagnosis for preventing vision loss.

Conclusions:

• Early detection of glaucoma is critical to prevent irreversible vision loss.
• Advanced classification and segmentation techniques are vital tools for accurate glaucoma diagnosis.
• Continued research into glaucoma findings and treatments is essential for patient care.