Application of Artificial Intelligence for Classification, Segmentation, Early Detection, Early Diagnosis, and Grading of Diabetic Retinopathy From Fundus Retinal Images: A Comprehensive Review
G.Kalaimathi Priya S.Rajarajeshwari, G. Chemmalar Selvi
Abstract
Diabetic Retinopathy (DR) remains a major factor contributing to vision loss worldwide, particularly among individuals with diabetes. Timely and accurate diagnosis of DR is essential to prevent vision loss and guide clinical intervention. In recent decades, Artificial Intelligence (AI) has demonstrated remarkable potential in revolutionizing DR disease identification and risk assessment. This review paper aims to perform the comprehensive analysis regarding the present status of research and developments in the field of DR using AI technologies. This comprehensive review emphasize the five categories of DR applications such as classification, detection, early diagnosis, segmentation and grading the severity of DR’s. This review article presents how AI technologies are leveraged in the current research works considering all these five DR applications. This is illustrated by using the AI-based DR workflow. It highlights the few significant case studies utilizing the AI technologies in real life problem-solving. Further, the adoption of Explainable Artificial Intelligence (XAI) techniques in the field of DR applications are discussed for model generalization and clinical validation. Finally, it presents the available benchmarking datasets, performance metrics, key challenges and future scope for DR research utilizing the AI technologies. This review work unveils several challenges and limitations with respect to input data modality, data preprocessing and data-fusion drawing the scope for further enhancements that can be made for a better DR prediction and detection.