A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Diabetes mellitus is a growing global health concern affecting all age groups.
• Diabetic Retinopathy (DR), a complication of long-standing diabetes, leads to vision impairment and blindness.
• Early detection of DR is vital to prevent severe visual complications.

Purpose of the Study:

• To survey the current literature on deep learning (DL) based methods for diabetic retinopathy diagnosis.
• To provide an overview of prevalent DL techniques and datasets used in DR detection.
• To compare the performance of reviewed DR detection methods.

Main Methods:

• Comprehensive literature review of studies employing deep learning for diabetic retinopathy detection.
• Analysis of commonly used deep learning architectures and algorithms.
• Compilation and description of publicly available datasets for DR image analysis.
• Performance evaluation of different methods using standard computer vision metrics.

Main Results:

• Deep learning models show significant promise for automated and accurate DR detection.
• Various DL techniques, including Convolutional Neural Networks (CNNs), are effectively applied to fundus images.
• Performance metrics indicate high accuracy in classifying DR severity across different studies.

Conclusions:

• Deep learning offers a powerful tool for the early and automated diagnosis of diabetic retinopathy.
• Further research and standardization of datasets and evaluation metrics can enhance clinical applicability.
• AI-driven DR detection systems have the potential to reduce the burden of visual impairment globally.