A Review on Fundus Image-Based Deep Learning for the Identification and Categorization of Diabetic Retinopathy
Keywords:
Deep Learning, retina, diabetic retinopathy, image processing, machine learningAbstract
Diabetes-related retinopathy, or diabetic retinopathy, is the most common cause of blindness in the world. Delaying or preventing eyesight loss and impairment calls for prompt diagnosis and treatment. For this reason, several AI-based approaches have been developed for identifying and categorizing diabetic retinopathy in fundus retina images. The application of deep learning techniques in the various stages of the fundus image-based diabetic retinopathy diagnosis pipeline is thoroughly investigated in this review study. From the commonly used datasets in the research community to the preprocessing techniques and how they accelerate and improve model performance, to the creation of deep learning models for diagnosis, grading, and lesion localization, we cover many of the key steps in this pipeline. Some models that have been used in actual clinical practice are also discussed. As a final step, we offer some key takeaways and suggestions for further study.
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