Retinal Image Processing Using Neural Networks For Disease Prediction
Keywords:
Image Processing, Eye Components, Disease Diagnosis, Diabetic PredictionAbstract
In medical field, diagnoses of diseases are competently carried out by using the image processing. Human eye is an important organ that reacts to light and has several purposes. The eye has a number of components but it is not limited to the cornea, iris, pupil, lens, retina, and macula, optic nerve, choroid and vitreous. Retinal images play vital role in several applications such as disease diagnose and human recognition. Retinal image analysis is particularly a complicated task because of the variability of the images in terms of the color, the morphology of the retinal anatomical pathological structure and the existence of particular features in different patients, which may lead to an erroneous interpretation. Image processing techniques were used for dark object detection to analyze the condition of the input image, to enhance the input image in order to make it suitable for processing of the retinal image, to improve visibility of Microaneyrysm in color fundus images. K-Nearest algorithm is used to detect the blood vessels effectively for segmentation process and Deep Neural algorithm is used to classify the diagnose the diseases such as stroke, heart attack and cardio vascular disease by segmenting optic disc and to predict retinal disease using Ellipse Fitting method. Experimental results show that good accuracy in disease prediction.
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