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An Improved Microaneurysms Detection for Diabetic Retinopathy Screening Using YOLO.
Sarni Suhaila Rahim1,2, Ankur Deo1,3, Rafia Mumtaz4
1Centre for Computational Science and Mathematical Modelling, Coventry University, Innovation Village 10, Coventry CV1 2TL, UK.
Biomedicines
|February 27, 2026
Summary
A new automated system effectively detects early diabetic retinopathy (DR) indicators like microaneurysms using YOLOv9. This AI approach significantly improves upon traditional methods for timely vision impairment prevention.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Diabetic retinopathy (DR) is a leading cause of vision loss, often stemming from undetected early pathological changes.
- Microaneurysms are the earliest detectable biomarkers of DR, signaling retinal abnormalities.
Purpose of the Study:
- To introduce a novel automated screening system for DR.
- To prioritize the detection of early indicators like microaneurysms.
Main Methods:
- Integration of advanced image processing techniques.
- Utilisation of the circular Hough transform and the YOLOv9 model for microaneurysm localisation and detection in fundus images.
Main Results:
- Development and evaluation of multiple system prototype versions.
- The best-performing YOLOv9 model achieved 91% accuracy, outperforming the circular Hough transform.
Conclusions:
- The developed models effectively overcome image processing challenges in lesion detection.
- The system addresses limitations of small and imbalanced datasets common in medical image analysis.
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