Automated diabetic retinopathy screening using deep learning

The purpose of this research is to propose a new method for identifying diabetic retinopathy using retinal fundus images. Currently, identifying diabetic retinopathy from computerized fundus images is a challenging task in medical image processing and requires new strategies to be developed. The manual analysis of the retinal fundus is time-consuming and requires a significant amount of skill. To assist clinicians, this research develops a graphical user interface that integrates imaging algorithms to assess whether the patient's fundus image is affected by diabetic retinopathy. The diagnosis is made using a deep neural network, specifically the Resnet152-V2, which has been shown to have 100% accuracy in all evaluation criteria including accuracy, recall, precision, and F1 Score. The severity of the disease is displayed on the graphical user interface and the patient's information is stored in a local database. This proposed method can also be used by ophthalmologists as a backup option to support in disease detection, reducing the necessary processing time.