Abstract
Abstract: Diabetic Retinopathy is a medical disorder in which diabetes mellitus causes damage to the retina. Diabetic Retinopathy is diagnosed using coloured fundus pictures, which requires trained clinicians to recognise the presence and importance of several tiny characteristics, making it a time-consuming task. We present a CNN-based technique to detect Diabetic Retinopathy in fundus pictures in this research. A new segmentation strategy using Gabor filters is employed to prepare the data used to train the model. Data augmentation is used to gather enough data to train the model due to the short dataset. Intricate characteristics in fundus images are detected by our segmentation model, which also detects the presence of DR. The model is efficiently trained using a high-end Graphics Processor Unit (GPU). Keywords: Augmentation, Segmentation, CNN
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