Content-based image analysis and computer vision techniques are used in various health-
care systems to detect the diseases. The abnormalities in a human eye are detected through …

Purpose To evaluate the performance of machine learning (ML) in detecting glaucoma using
fundus and retinal optical coherence tomography (OCT) images. Design Meta-analysis …

Glaucoma is an eye disease in which fluid within the eye rises and puts pressure on optic
nerves. This fluid pressure slowly damages the optic nerves, and if it is left untreated, it may …

Feature selection is an important component of the machine learning domain, which selects
the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a …

Glaucoma is an ocular disorder that affects the optic nerve and ultimately leads to partial or
complete vision loss. Hence, there is a strong need for early screening of glaucoma. Earlier …

Glaucoma is the second most common cause of vision loss. Manual screening of a patient's
eye or screening through a fundus image of the patient's eye requires expert …

Glaucoma, commonly known as the silent thief of sight, is the second most common cause of
blindness in humans, and the number of cases is steadily increasing. Conventional …

Reduced computational time and cost, reduced skilled professional resources, and
diagnostic accuracy have made medical diagnosis using computer aided systems (CAD) …

Glaucoma is one of the leading causes of irreversible vision loss, it progresses gradually
without easily noticeable symptoms. The detection of glaucoma in the early stage is crucial …

Feature selection (FS) is crucial to transforming high-dimensional data into low-dimensional
data. The FS approach selects influential traits and ignores the rest. This approach improves …