Abstract
Diabetic Retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes. Early detection and timely intervention are critical for preventing severe complications. However, the scarcity of ophthalmologists, especially in rural and underdeveloped regions, limits the accessibility of timely diagnosis and treatment. Telemedicine, combined with advanced artificial intelligence (AI) techniques, such as Convolutional Neural Networks (CNN), offers a promising solution for improving DR detection and patient care remotely. This paper presents a comprehensive survey of recent advancements in DR detection using CNN-based deep learning models, along with telemedicine frameworks for real-time consultation and treatment suggestions. We discuss the underlying architecture of CNN models and their role in automating retinal image classification.
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