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Erschienen in:
01.12.2024 | Original Paper
Resilient back-propagation machine learning-based classification on fundus images for retinal microaneurysm detection
Erschienen in: International Ophthalmology | Ausgabe 1/2024
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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.