Abstract
Diabetic Retinopathy is an ocular manifestation of diabetes . The longer a person has diabetes, higher are the chances of having diabetic retinopathy in their visual system. Hence the objective of this research work is to propose an automated, suitable and sophisticated approach using image processing so that diabetic retinopathy can be detected at early levels easily and damage to retina can be minimized. A vital point of diabetic retinopathy that it causes detectable changes in the blood vessels of the retina. The focal blurred edges are detected so as to dismiss the false alarms. A two-level approach is used here to classify data. Firstly, optimal features are extracted from the training data and secondly, the classification is done by the use of the adaptive super pixel algorithm and then the test data is analyzed. Adaptive super pixel algorithm can adjust the weights of various features based on their discriminating ability. After the application of algorithm, the diabetic eye is detected by means of various parameters like colour, texture, spatial distance, contour, mean, standard deviation, entropy and maximum pixel points. This research can aid the doctor for easy detection of the disease as it given an accuracy of about 98.33%.
Highlights
Diabetes occurs among various people due to various factors like sedentary life style, stress and eating habits
Diabetic Retinopathy usually happens if a person has diabetes for more than ten years or more
The optic disk is removed for better enhancement and classification 5.2.8 Blood Vessel Extraction
Summary
Diabetes occurs among various people due to various factors like sedentary life style , stress and eating habits. Diabetic Retinopathy usually happens if a person has diabetes for more than ten years or more. Diabetic Retinopathy (DR), causes around five percent of blindness among people and this is one of the major reason that causes blindness among diabetics. According to World health Organization (WHO) estimation, 425 million of world population is having the diabetes [1] .Research indicates that. * Correspondence Author Dr Balambigai Subramanian, Associate Professor, Department of. Associate Professor, Department of Computer Science and Engineering, K L Deemed to be University, Guntur (Andhra Pradesh) India. Dr Rudra Kalyan Nayak, Associate Professor, Department of Computer Science and Engineering, K L Deemed to be University, Vaddeswaram (Andhra Pradesh) India
Published Version
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