Study of clinical staging and classification of retinal images for retinopathy of prematurity ROP screening

ABSTARCT :

Retinopathy of Prematurity (ROP) is a disease which requires immediate precautionary measures to prevent blindness in the infants, and this condition is prevalent in premature babies in all the underdeveloped, developing, and in the developed countries as well. This paper proposes a tool by which the stage and zones of Retinopathy of Prematurity in infants can be diagnosed easily. This tool takes the input from the Retcam and detects the stage, zone, and gives a rating of 1 to 9 for classifying the severity of the disease in the infants. This is achieved by extracting the optic disc, marking the ridge, and the distance of the optic nerve. This tool can be easily used by nurses and paramedics, unlike the existing technologies which require the guidance of a specialist to come to a conclusion.

EXISTING SYSTEM :

Retinopathy of Prematurity (ROP) is a disease which requires immediate precautionary measures to prevent blindness in the infants, and this condition is prevalent in premature babies in all the underdeveloped, developing, and in the developed countries as well.

DISADVANTAGE :

This has become increasingly prevalent. It may be less affected with no effects to the visual, or very prevalent with neovascularization which is new vessel formation and can move on to become retinal detachment or blindness.

PROPOSED SYSTEM :

ROP plus disease likely exists as a continuous spectrum of retinal vascular abnormality. Thus, the performance of the convolution neural network algorithm in detecting intermediate and less severe pre-plus disease was evaluated. The statistical performance of the classifier was evaluated in an expanded external test set of fungal images, which comprised of the initial external test set of normal retina image and disease images, with an additional preplus disease fungal images. Statistical performance was measured by calculating sensitivity, specificity, accuracy, positive predictive value, and negative predictive value measured.

ADVANTAGE :

? Our tool is based on image processing which is easy and simple for processing the image using Matlab. We have used Matlab 2015a in which the image processing tool provides us a reference for algorithms which is a standard set, functions which can be used, and a good platform for image processing, image analysis, visualization of image, and algorithm calculation.

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