Detection and classification Of Diabetic Retinopathy Diseases By Using SVM(Support Vector Machine)

Abstract

In this research article, a brief insight into the detection of DR in human eyes using different types of
preprocessing & segmentation techniques is being presented. Here we address the detection of
Hemorrhages and micro aneurysms in color fundus images. In pre-Processing we separate red, green,
blue color channel from the retinal images. The green channel will pass to the further process. The
green color plane was used in the analysis since it shows the best contrast between the vessels and the
background retina. Then we extract the GLCM (Gray Level Co-Occurrence Matrix) feature. In the
GLCMs, several statistics information is derived using the different formulas. These statistics provide
information about the texture of an image. Such as Energy, Entropy, Dissimilarity, Contrast, Inverse
difference, correlation Homogeneity, Auto correlation, Cluster Shade Cluster Prominence, Maximum
probability, Sum of Squares will be calculated for texture image. After feature Extraction, we provide
this feature to classifier. Finally it will predict about the retinal whether it is hemorrhages or
microaneurysms . After predicting the about the retinal image we will localize the affected place. For
segmenting the localized place we will use segmentation. One of the important organ of the human
being is the eye. It has to be noted that if the eyes are not there, then the whole world would be dark &
the human life even though it is existing will be a waste. Different types of the diseases occurs in the
eyes. One of the deadliest disease which occurs in the eyes is the DR. This disease is the second largest
disease which is occurring amongst the human beings as per the WHO – United Nations survey. Hence,
atmost importance has to be given to the eye care. This disease occurs due the reduction of the nerve
area in the retina.. In this paper, a mere introduction is given to the diabetic retinopathy disease.