Deep Learning based Diabetic Retinopathy Detection using Image Processing

The global prevalence of visual impairment and blindness due to diabetic retinopathy can be significantly reduced through improved diabetes management. Diabetic retinopathy (DR) screening is crucial as it can prevent vision loss. In recent years, personalized screening intervals considering various risk factors have been proposed, showing promising cost-effectiveness ratios. However, limited funding hampers the implementation of widespread screening programs in many countries. A groundbreaking advancement in screening methods is the application of Deep Learning, which revolutionizes the detection and classification of diabetic retinopathy while reducing costs. By automating the analysis of retinal images, Deep Learning techniques enable efficient and accurate identification of DR, enabling timely intervention and treatment. Moreover, emerging research suggests that retinal imaging can not only detect existing diabetic retinopathy but also identify individuals at risk, expanding the scope of screening beyond the detection of sight-threatening diseases. This paves the way for proactive measures to prevent or mitigate the progression of DR. Therefore, this research aims to develop a Deep Learning-based system for diabetic retinopathy detection using image processing techniques. By leveraging the power of Deep Learning algorithms and advanced image analysis, this study seeks to improve the accuracy, efficiency, and accessibility of DR screening, ultimately enhancing patient outcomes and reducing the burden on healthcare systems..