An improved microaneurysms detection for diabetic retinopathy screening using YOLO

Rahim, Sarni Suhaila and Deo, Ankur and Palade, Vasile and Mumtaz, Rafia (2026) An improved microaneurysms detection for diabetic retinopathy screening using YOLO. Biomedicines, 14 (2). pp. 1-19. ISSN 2227-9059

Text 01756160220261350323027.pdf Available under License Creative Commons Attribution. Download (3MB)

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.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Diabetic retinopathy, Microaneurysms detection, Retinal fundus imaging, YOLO
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	13 Jul 2026 06:24
Last Modified:	13 Jul 2026 06:24
URI:	http://eprints.utem.edu.my/id/eprint/29784
Statistic Details:	View Download Statistic

Actions (login required)

View Item