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Cited by 335

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An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading

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Antal, B., Hajdu, A.·2012·IEEE Transactions on Biomedical Engineering

Abstract

Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first, and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy (DR) grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 ± 0.01 is achieved in a "DR/non-DR"-type classification based on the presence or absence of the microaneurysms.

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Metadata

Authors
Antal, B., & Hajdu, A.
Venue
IEEE Transactions on Biomedical Engineering
Published
2012-06
Volume
59
Issue
6
Pages
1720-1726
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
OpenAlex
W1996151979
Cited by
335
References
1
FWCI
13.57
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading. https://app.cochat.ai/papers/10.1109/tbme.2012.2193126
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Antal, B., & Hajdu, A. (2012). An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2012.2193126

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