Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Diabetic retinopathy (DR) is a leading cause of preventable blindness.
• Early detection and treatment of DR are essential to preserve vision.
• Computer-aided diagnosis (CAD) systems using retinal fundus images aid in early DR diagnosis.

Purpose of the Study:

• To review deep learning (DL) based computer-aided diagnosis (CAD) methods for diabetic retinopathy (DR).
• To highlight the advantages and disadvantages of various DL approaches for DR diagnosis.
• To identify challenges and future research directions in DL for DR detection.

Main Methods:

• Review of existing literature on deep learning (DL) algorithms applied to diabetic retinopathy (DR) diagnosis.
• Analysis of CAD system stages: lesion detection, segmentation, and classification.
• Comparison of traditional machine learning (ML) with emerging DL techniques.

Main Results:

• Deep learning methods show significant promise and effectiveness in DR diagnosis.
• Various DL architectures have been developed for DR detection, segmentation, and classification.
• Traditional ML methods rely on hand-engineered features, while DL methods learn features automatically.

Conclusions:

• Deep learning offers a powerful approach for improving the accuracy and efficiency of DR diagnosis.
• Further research is needed to address challenges in developing robust and effective DL algorithms for DR.
• Future work should focus on optimizing DL models for real-world clinical application in DR screening.