Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
A comprehensive review on automated diabetic retinopathy detection and classification using fundus image.
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Dixit Ravi Bhushan, Jha Chandan Kumar
Аннотация
PURPOSE: Diabetic Retinopathy (DR) is a vision-threatening complication in diabetic patients. It harms retinal vessels and may lead to blindness. Detection at an early stage and its classification can prevent the risk of vision loss. However, fundus image-based manual screening of DR is a time-consuming and complex process.
METHODS: In recent years, many automated techniques for DR detection have been developed to screen and diagnose the disease condition at an early stage. These techniques are explored using the keywords diabetic retinopathy, fundus image, ophthalmology with machine learning (ML), and deep learning (DL). Search engines such as Google Scholar, PubMed, Medline, IEEE Explore, and Science Direct are utilised and explored to gather existing research papers.
RESULTS: This review systematically examines several techniques for DR detection and classification, using ML, DL, and hybrid approaches. This study analyses methodologies, datasets, pre-processing steps, performance evaluation metrics of existing techniques, and challenges associated with overfitting, model complexity, class imbalance, and deep feature extraction. Recent advancements in ensemble learning, transformer-based techniques, and attention mechanisms are also discussed for DR detection and classification.
CONCLUSION: The review explored and discussed the challenges of existing DR detection and classification methods. This paper suggests future research directions to improve the accuracy and robustness of DR detection systems.
METHODS: In recent years, many automated techniques for DR detection have been developed to screen and diagnose the disease condition at an early stage. These techniques are explored using the keywords diabetic retinopathy, fundus image, ophthalmology with machine learning (ML), and deep learning (DL). Search engines such as Google Scholar, PubMed, Medline, IEEE Explore, and Science Direct are utilised and explored to gather existing research papers.
RESULTS: This review systematically examines several techniques for DR detection and classification, using ML, DL, and hybrid approaches. This study analyses methodologies, datasets, pre-processing steps, performance evaluation metrics of existing techniques, and challenges associated with overfitting, model complexity, class imbalance, and deep feature extraction. Recent advancements in ensemble learning, transformer-based techniques, and attention mechanisms are also discussed for DR detection and classification.
CONCLUSION: The review explored and discussed the challenges of existing DR detection and classification methods. This paper suggests future research directions to improve the accuracy and robustness of DR detection systems.