Detection of Medical Diabetic Retinopathy through Application Using Deep Learning Models

Deep learning (DL) allows for the creation of computer models that have many processing layers and are capable of learning data representations at different levels of abstraction. DL algorithms have also significantly improved capabilities for screening, recognition, segmentation, prediction, and classification across numerous healthcare domains, such as those pertaining to the abdominal, heart, pathology, and retina. One of the crucial reasons of blindness in working-age individuals is diabetic retinopathy. For a good prognosis, early diagnosis of this illness is essential. In this study, researchers show how to recognize diabetic retinopathy staging using colour fundus pictures and convolutional neural networks (CNNs). Having Validation sensitivity of 95%, our network models produced test metric performance comparable to baseline literature values.