# An Improved Microaneurysms Detection for Diabetic Retinopathy Screening Using YOLO

## TL;DR

This paper presents an improved automated system for detecting early signs of diabetic retinopathy using the YOLOv9 model, achieving high accuracy.

## Contribution

A novel automated screening system using YOLOv9 for microaneurysms detection in diabetic retinopathy, outperforming traditional methods.

## Key findings

- The YOLOv9-based model achieved 91% accuracy in detecting microaneurysms.
- The model effectively handles challenges like small lesion sizes and class imbalance in medical images.

## Abstract

Background/Objectives: Diabetic retinopathy (DR) is a chronic, progressive complication of diabetes mellitus and remains one of the leading causes of vision impairment worldwide, particularly when early pathological changes go undetected or untreated. The earliest clinically identifiable biomarkers are microaneurysms, which are minute, round dilatations of capillary walls. Retinal abnormalities of a broad spectrum are indicative of the condition. This paper introduces a novel automated screening system for DR that prioritises the detection of these early indicators. Methods: The proposed approach integrates advanced image processing techniques based on the circular Hough transform and the YOLOv9 model, to localise and detect microaneurysms in colour fundus images. Results: Several system prototype versions were developed and evaluated. The final, best-performing YOLOv9-based model achieved an accuracy of 91%, representing a substantial performance improvement compared with the circular Hough transform. Conclusions: The developed models effectively address the issue of significant image processing challenges in lesion detection as well as small and class imbalance data, which are recurring constraints in medical image analysis.