A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using...

Area of Science:

• Ophthalmology and Medical Imaging
• Artificial Intelligence in Healthcare
• Computational Biology and Bioinformatics

Background:

• Diabetic retinopathy (DR) is a primary cause of global vision impairment, linked to long-term diabetes and poor blood glucose control.
• Manual DR assessment from fundus images is labor-intensive, requiring specialized expertise, and poses challenges for timely diagnosis.
• The increasing prevalence of diabetes and its ocular complications necessitates efficient and scalable diagnostic solutions.

Purpose of the Study:

• To review artificial intelligence (AI) methods, including machine learning (ML), deep learning (DL), and federated learning (FL), for diabetic retinopathy detection.
• To examine the application of various neural network architectures, such as CNNs, RNNs, and GANs, in analyzing fundus images for DR.
• To discuss current datasets, performance metrics, biomarkers, screening strategies, and future research directions in AI-driven DR diagnosis.

Main Methods:

• Comprehensive review of AI methodologies applied to fundus image analysis for diabetic retinopathy.
• Examination of deep learning models, including convolutional neural networks (CNNs) and their variants, for feature extraction (blood vessels, microaneurysms, hemorrhages).
• Analysis of machine learning, deep learning, and federated learning approaches for disease detection, grading, and evaluation.

Main Results:

• AI, particularly DL models like CNNs, demonstrates significant potential in automating the analysis of fundus images for DR.
• These methods can accurately identify and grade DR by analyzing critical features such as blood vessels, exudates, and hemorrhages.
• Federated learning offers a privacy-preserving approach for collaborative model training across multiple institutions.

Conclusions:

• AI-powered tools, leveraging ML, DL, and FL, provide a promising avenue for efficient and accurate diabetic retinopathy screening and diagnosis.
• Further research into developing robust DL models and integrating diverse data sources is crucial for enhancing diagnostic performance.
• Addressing challenges and exploring future directions will optimize AI's role in combating vision loss from diabetic retinopathy.