Diabetic Retinopathy (DR) poses a major hazard to global public health,
causing considerable vision loss if left concealed. Deep learning algorithms have
emerged as capable tools for recovering the accuracy of DR analysis and prediction.
This chapter explores the potential of deep learning in enhancing predictive accuracy
for DR, reviewing modern advancements and prominent key challenges, and future
directions. Researchers inspect diverse deep learning architectures engaged for DR
prediction, together with Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), and ensemble methods. The authors attribute the underperformance
of deep learning in DR prediction to some collision aspects such as data quality,
interpretability, and ethical issues. Finally, the authors suggest a host of elucidatory
opportunities and support Investigative Restorative practices as well as Team-Based
Care for improved DR management.
Keywords: Convolutional Neural Networks (CNNs), Diabetic Retinopathy (DR), Deep learning, Ensemble methods, Predictive medicine, Recurrent Neural Networks (RNNs).
