Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 17;44(1):91.
doi: 10.1007/s10792-024-02982-5.

Resilient back-propagation machine learning-based classification on fundus images for retinal microaneurysm detection

S Steffi  1 , W R Sam Emmanuel  2
Affiliations

Resilient back-propagation machine learning-based classification on fundus images for retinal microaneurysm detection

S Steffi et al. Int Ophthalmol. .

Abstract

Background: The timely diagnosis of medical conditions, particularly diabetic retinopathy, relies on the identification of retinal microaneurysms. However, the commonly used retinography method poses a challenge due to the diminutive dimensions and limited differentiation of microaneurysms in images.

Problem statement: Automated identification of microaneurysms becomes crucial, necessitating the use of comprehensive ad-hoc processing techniques. Although fluorescein angiography enhances detectability, its invasiveness limits its suitability for routine preventative screening.

Objective: This study proposes a novel approach for detecting retinal microaneurysms using a fundus scan, leveraging circular reference-based shape features (CR-SF) and radial gradient-based texture features (RG-TF).

Methodology: The proposed technique involves extracting CR-SF and RG-TF for each candidate microaneurysm, employing a robust back-propagation machine learning method for training. During testing, extracted features from test images are compared with training features to categorize microaneurysm presence.

Results: The experimental assessment utilized four datasets (MESSIDOR, Diaretdb1, e-ophtha-MA, and ROC), employing various measures. The proposed approach demonstrated high accuracy (98.01%), sensitivity (98.74%), specificity (97.12%), and area under the curve (91.72%).

Conclusion: The presented approach showcases a successful method for detecting retinal microaneurysms using a fundus scan, providing promising accuracy and sensitivity. This non-invasive technique holds potential for effective screening in diabetic retinopathy and other related medical conditions.

Keywords: Adaptive thresholding; Fundus image; Gradient; Machine learning; Retinal microaneurysms.

PubMed Disclaimer

References

    1. Lee R, Wong TY, Sabanayagam C (2015) Epidemiology of diabetic retinopathy, diabetic macular edema, and related vision loss. Eye and vision 2(1):17 - PubMed - PMC
    1. Wilkinson C, Ferris FL, Klein RE et al (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110(9):1677–1682 - PubMed
    1. Yau JW, Rogers SL, Kawasaki R et al (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35(3):556–564 - PubMed - PMC
    1. Ahmed NGA, Hamza MF, Hassan SN (2023) Knowledge, practice and attitude of diabetic patients regarding prevention of diabetic retinopathy. J Surv Fisher Sci 10(3S):3896–3908
    1. Lyssekboron A, Wylegala A, Polanowska K, Krysik K, Dobrowolski D (2017) Longitudinal changes in retinal nerve fiber layer thickness evaluated using avanti rtvue-xr optical coherence tomography after 23g vitrectomy for epiretinal membrane in patients with open-angle glaucoma. J Healthc Eng 2017:4673714–4673714 - PubMed

LinkOut - more resources