International Journal of Computer Applications (0975 – 8887) Volume 125 – No.15, September 2015 7 Automated Diagnosis Non-proliferative Diabetic Retinopathy in Fundus Images using Support Vector Machine Raju Sahebrao Maher Department of Computer Science& IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad, India Sangramsing N. Kayte Department of Computer Science& IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad, India Sandip T. Meldhe S.D.Deshmukh College Bhokardan Dr. Babasaheb Ambedkar Marathwada University Aurangabad, Maharashtra, India Mukta Dhopeshwarkar, PhD Department of Computer Science& IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad, India ABSTRACT Diabetic retinopathy (DR) is caused by damage the retina because fluid leaks from blood vessels into the retina. Damage the posterior part of the eye of the diabetic patient. This disease that occurs when does not secrete enough insulin or the body is unable to process it properly. The main two types of diabetic retinopathy the first are non-proliferate diabetes retinopathy (NPDR) and second are proliferate diabetes retinopathy (PDR). The increasing number of DR cases world-wide demands to the development of an automated detection system. We have proposed a computer based method for the detection of diabetic retinopathy using the fundus images. Using Image pre-processing, morphological processing techniques involves processing of fundus images for detect features, such as blood vessel area, exudates, microaneurysms, hemorrhages, and texture. Proposed techniques used for the extraction of these features from digital fundus images. The proposed techniques have been tested on the images of DIARETDB0 database. In which have total 130 images they all images are tested and it’s classified into microaneurysms, hemorrhages, and texture using Support Vector Machine (SVM) for an automatic classification. The detection results obtained by comparing it with expert ophthalmologists. We demonstrated a classification sensitivity of 96.43%, specificity of 95.9 % and accuracy of 99.27 %. Keywords Diabetic Retinopathy, Fundus images, Microaneurysms, Exudates, Retinal blood vessels. Image morphology, artificial neural network. 1. INTRODUCTION Diabetic retinopathy is a complication of diabetes and a leading cause of blindness in the world. It occurs when diabetes damages the tiny blood vessels inside the retina. This tiny blood vessel will leak blood and fluid on the retina retinopathy are occur such as microaneurysms, hemorrhages, hard exudates, cotton wool spots. The number of people afflicted with the disease continues to grow it. Blindness is an effect of diabetic retinopathy and its prevalence is set to continue rising. Estimated 50–65 new cases of blindness per 100,000 people happened every year [1]. The World Health Organization (WHO) expects the number of people with diabetics to increase from 130 million to 350 million over the next 25 years. [1]. Diabetic retinopathy (DR) is a common complication of diabetes. Indeed, it is so common that it is the leading cause of blindness in the working population of western countries [2]. Diabetes is increasing in developed countries, as well as in underdeveloped countries. It is estimated that 75% of people with diabetic retinopathy live in developing countries [3]. Diabetes Retinopathy is a silent disease, because it may only be recognized by the patient when the changes in the vision. Diabetic Retinopathy has been proved to 17% to 97% of the cases after 5 and 15 years of the diagnosis of diabetes respectively. It have progressed to a level where treatment is difficult and nearly impossible. The impairment of vision and blindness can be prohibited or detected if accordingly regular screening and treated of eyes. Automated detection will lead to a large amount of savings of time and effort. In recent years, the growing of number of diabetic patients has greatly motivated the research work in automatic developing tools and methodologies to facilitate the screening problems. This paper main proposes an automated technique for microaneurysms and hemorrhages in retinal images using an image pre-processing techniques such as thresholding and morphological reconstruc-tion, and boundary sketching to detect the dark lesions, such as microaneurysms and hemorrhages using a SVM classifier to classify the retinal images into normal and abnormal (i.e. NPDR and PDR). The NPDR severity scale is further classified as mild and moderate. RGB image is convert in the green channel image good contrast between the background and the dark lesions retinal components, such as microaneurysms and hemorrhages, it is reliable to work on the green channel of the RGB color space in order to detect the dark objects easily. The proposed classification algorithm is classify the various disease. 1.1 Diabetes Diabetes mellitus (DM) is the name of a chronic, systemic, life-threatening disease. Metabolism refers to the way our bodies use digested food for energy and growth. Most of what we eat is broken down into glucose. Glucose is a form of sugar in the blood - it is the principal source of fuel for our bodies. It occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. This results in an abnormal increase in the glucose level in the blood. Over time this high level of glucose causes damage to blood vessels. This damage affects both eyes and nervous