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Detection of Diabetic Retinopathy in Retinal Fundus Image Using YOLO-RF Model

Diabetic Retinopathy (DR) is one of the complications of diabetes that impacts blood vessels of a retina because of increased blood sugar. So, it’s better to detect and treat at the initial stage. The biggest challenges are inadequate technology assistance for ophthalmologists and difficulty in the manual identification process. These issues can be addressed by technological advancement in the field of Artificial Intelligence for automizing the identification and detection process. An automatic detection helps to identify different stages of DR and helps ophthalmologists to provide treatment according to the stages in order to avoid vision loss. In this paper, proposed system aims to detect the various stages of DR that allows ophthalmologists to identify the DR at its different stage. The proposed system classifies the image data into defined classes using YOLO-RF. The proposed system compared with various traditional machine learning classifiers such as SVM, Decision Tree (DT), Random Forest (RF) and DL model such as YOLO. We have used data from the retinal fundus images of KAGGLE and IDRID. The result showed that proposed system YOLO-RF model performed with good accuracy of 99.3%, precision score of 97.2 and Recall of 99.1.