Decision Support System for Blood Vessel and Optic Disc Segmentation

Recently, diabetes is rapidly increasing. Diabetes has many types that infect different organs. Diabetic retinotopic is one of them. Diabetic retinotopic and glaucoma are eye diseases that can lead to blindness. Optic disc segmentation helps identify these diseases. However, here some obstacles arise, such as illumination variations. The effect of blood vessels in fundus images should be removed. This is because blood vessels can incise the edges of the optic disc insert ignore into journalissuearticles values(O.D.);. This study aimed to remove blood vessels from images and then successfully segment the optic disc. We developed a new approach called LinkNetRCB7 based on LinkNet. LinkNetRCB7 has achieved successful results. The accuracy for blood vessel segmentation and O.D. was calculated to be 98.5% on the Stare dataset and 98.850% on DRISHTI GS. A decision support system insert ignore into journalissuearticles values(DSS); including these stages has been proposed within the scope of the study. The use of DSS in disease diagnosing is becoming widespread. No decision support system in the literature includes deep learning and image processing algorithms for diabetic retinopia. With the proposed decision support system, a system that can help decision-makers diagnose diabetic retinotopic has been designed.