Computer science and Engineering
MACHINE LEARNING CATEGORIZER EARLY GLAUCOMA DIAGNOSIS
It
is difficult for biomedical engineers to identify physiological changes
occurring inside the human body. To be more precise, finding abnormalities in
the human eye is exceedingly challenging and time consuming due to the
procedures many complications. Due to the need for early disease detection
technologies that aid in screening and disease management, retinal image
analysis attracted greater attention from researchers. Numerous studies are
conducted in retinal image analysis in order to automate the diagnosis of
glaucoma. In India, glaucoma sickness ranks third in the list of diseases that
cause blindness in humans globally. Thus, it is crucial to find glaucoma early
on in order to stop eye disorders from getting worse. According to reports,
glaucoma worsens the cup to disc ratio in the eye, which impairs peripheral
vision. The book addresses a variety of image processing approaches that are
employed in glaucoma diagnosis based on Cup to Disc ratio evaluation of the
pre-processed retinal fundus pictures. On publicly available fundus imaging
datasets, these computational approaches are assessed, and the performance
outcomes are contrasted.
kps.10.22/2935
– Dr.Leena Nesamani , Miss.Dhrishya S Devan , Mrs.R.Jayapratha , Mr.Sriram surya