Diabetic-Retinopathy Classification Using a Modified VGG16 Model

Diabetes has an impact on the eyes and may cause diabetic retinopathy (DR). It happens when the blood capillaries in the retina, the tissue that reacts to light at the back of the eye, are damaged, resulting in blindness. However, due to slow progression, the disease shows few signs in the early stages, hence making disease detection a difficult task. Therefore, a fully automated system is required to support the detection and screening process at early stages. In this paper, an automated DR classification using a modified VGG16 model was proposed. The proposed system employed DR 224 by 224 Gaussian Filtered dataset images. These images are pre-processed before applying to the VGG 16 pretrained model. The pre-trained VGG 16 model employed for the classification of DR images. The proposed model achieves an accuracy of 91.11 % and a loss of 20.17%. The proposed model helps physicians identify specific class DR.