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The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus image. Large number of people suffers from eye diseases in rural and semi urban areas all over the world. Current diagnosis of retinal diseases relies upon examining retinal fundus image. In this project, a novel method for automatic detection of red lesion and glaucoma in color retina images is described and validated.Red lesion is any abnormal damage or change in the tissue of an organism, usually caused by disease. Glaucoma is a neurodegenerative disorder of the optic nerve, which causes partial loss of vision. The main contribution of projects is a newest of dynamic shape and GLCM features. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. Multilevel SVM used for final validation of the proposed method. Different output parameters such as accuracy, sensitivity and specificity are measured. Finally the improved output parameters were obtained.

MULTICLASS SVM BASED RED LESION DETECTION AND GLAUCOMA DETECTION FOR DIABETIC PATIENT