ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy
Manisha Laxman Jadhav1, M. Z. Shaikh2

1Manisha Laxman Jadhav*, Department of E&TC Engineering, MET’s Institute of Engineering, Nashik. India.
2Dr. M. Z. Shaikh,  Principal,D.Y.Patil School of Engineering, Lohgaon, Pune, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 5590-5595 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2203109119/2019©BEIESP | DOI: 10.35940/ijeat.A2203.109119
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Abstract: The higher levels of blood glucose most often causes a metabolic disorder commonly called as Diabetes, scientifically as Diabetes Mellitus. A consequence of this is a major loss of vision and in long terms may eventually cause complete blindness. It initiates with swelling on blood vessels, formation of microaneurysms at the end of narrow capillaries. Haemorrhages due to rupture of small vessels and fluid leak causes exudates. The specialist examines it to diagnose and gives proper treatment. Fundus images are the fundamental tool for proper diagnosis of patients by medical experts. In this research work the fundus images are taken for processing, the neural network and support vector machine are trained for the proposed model. The features are extracted from the diabetic retinopathy image by using texture based algorithms such as Gabor, Local binary pattern and Gray level co-occurrence matrix for rating the level of diabetic retinopathy. The performance of all methods is calculated based on accuracy, precision, Recall and f-measure.
Keywords: Diabetic Retinopathy (DR), SVM, Neural Networks (ANN), Gabor, Statistical, LBP.