Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Diabetic Retinopathy (DR) is a leading cause of vision loss, affecting one-third of 285 million diabetes patients globally.
• Manual DR diagnosis is time-consuming, expensive, and requires expert ophthalmologists.
• Early detection and treatment are crucial for reducing DR-related visual impairment.

Purpose of the Study:

• To systematically review and analyze deep learning approaches for DR diagnosis.
• To comprehend DR grading, staging protocols, and taxonomy.
• To identify, compare, and investigate deep learning algorithms for classifying DR stages.

Main Methods:

• Systematic review of high-quality research on deep learning for DR diagnosis.
• Analysis of publicly available datasets for deep learning applications in DR.
• Comparison of deep learning algorithms, including Convolutional Neural Networks (CNNs), Ensemble CNNs (ECNNs), and Deep Neural Networks (DNNs).

Main Results:

• Deep learning approaches show increasing adoption for DR diagnosis.
• CNNs were used in 35% of studies, ECNNs in 26%, and DNNs in 13%.
• Analysis provides empirical understanding for real-time DR applications.

Conclusions:

• Deep learning algorithms offer significant potential for the early detection and prevention of Diabetic Retinopathy.
• The study highlights the growing trend and effectiveness of AI in ophthalmic diagnostics.
• Further research in deep learning for DR diagnostics can lead to improved patient outcomes.