Transfer learning-driven ensemble model for detection of diabetic retinopathy disease

In this study, we propose an ensemble model for the detection of diabetic retinopathy (DR) illness that is driven by transfer learning. Due to diabetes, the DR is a problem that affects the eyes. The retinal blood vessels in a person with high blood sugar deteriorate. The blood arteries may enlarge and leak as a result, or they may close and stop the flow of blood. If DR is not treated, it can become severe, damage vision, and eventually result in blindness. Medical experts study the colored fundus photos for this reason in order to manually diagnose disease, however this is a perilous technique. As a result, the condition was automatically identified utilizing retinal scans and a number of computer vision-based methods. A model is trained on one task or datasets employing the transfer learning (TL) technique, and then the pre-trained models or weights are applied to another task or dataset. Six deep learning (DL)-based convolutional neural network (CNN) models were trained in this study using huge datasets of reasonable photos, including DenseNet-169, VGG-19, ResNet101-V2, Mobilenet-V2, and Inception-V3. We also applied a data-preprocessing strategy to improve the accuracy and lower the training costs in order to improve the results. The experimental results demonstrate that the suggested model works better than existing approaches on the same dataset, with an accuracy of up to 98%, and detects the stage of DR.