DESIGN AND EVALUATION OF POST-PROCESSING METHODS BASED ON THE RANDOM FOREST CLASSIFIER TO DECREASE FALSE POSITIVES IN MICROANEURYSM SEGMENTATION USING A U-NET MULTIRESOLUTION MODEL - Siti Sarah, Ines
Microaneurysms (MAs), which are swellings in the blood vessels visible on the fundus of the eye, are one of the indicators for the early detection of diabetic retinopathy (DR), a complication of diabetes that causes a decrease in patients' visual ability. In previous research on MA detection in the IDRiD dataset (Ramadiastri, 2022), a MA detection model was developed using a multiresolution U-Net model, with a post-processing method of majority hard voting by 3 resolutions (640x640 px, 960x960 px, and 1280x1280 px). It was found that the precision value of the segmentation method was still low. The main objective of this