Visionary AI Enhancing Diabetic Retinopathy Detection through Image Processing

The development of Diabetic Retinopathy (DR) necessitates comprehensive screening techniques to avoid visual loss. Existing manual screening techniques are subjective and prone to errors, resulting in delayed diagnosis and poor patient outcomes. In response, the paper introduces a Visionary AI system that transforms DR detection via image processing methods and Convolutional Neural Networks (CNNs). The proposed system decreases the pressure on healthcare personnel by automating analyses and assuring consistency and accuracy. The proposed system outperforms existing systems, achieving 95% accuracy, 92% sensitivity, 96% specificity, and an AUC-ROC value of 0.97. Furthermore, the proposed system demonstrates scalability and adaptability, indicating a larger application in a variety of healthcare contexts. Future research aims to expand the proposed system's capabilities and incorporate it seamlessly into healthcare workflows, with a focus on its ability to greatly improve DR detection and patient outcomes.