The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
An ensemble-based system for automatic screening of diabetic retinopathy
Open accessAbstract
In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and anatomical (macula, optic disk) components. The actual decision about the presence of the disease is then made by an ensemble of machine learning classifiers. We have tested our approach on the publicly available Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and 0.989 AUC are achieved in a disease/no-disease setting. These results are highly competitive in this field and suggest that retinal image processing is a valid approach for automatic DR screening.
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Cites
Combining Pattern Classifiers: Methods and Algorithms
Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,005
Retinal Imaging and Image Analysis
Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2010.2084567 · Cited by 1,439
Classifier selection for majority voting
Ruta, D., & Gabrys, B. (2005) · Information Fusion · doi:10.1016/j.inffus.2004.04.008 · Cited by 630
An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading
Antal, B., & Hajdu, A. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2193126 · Cited by 335
Self-citationAutomated Early Detection of Diabetic Retinopathy
Abràmoff, M. D., Reinhardt, J. M., Russell, S. R., Folk, J. C., Mahajan, V. B., Niemeijer, M., & Quellec, G. (2010) · Ophthalmology · doi:10.1016/j.ophtha.2010.03.046 · Cited by 287
Abràmoff, M. D., Niemeijer, M., Suttorp-Schulten, M. S. A., Viergever, M. A., Russell, S. R., & van Ginneken, B. (2008) · Diabetes Care · doi:10.2337/dc07-1312 · Cited by 277
Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection
Agurto, C., Murray, V., Barriga, E., Murillo, S., Pattichis, M., Davis, H., Russell, S., Abramoff, M., & Soliz, P. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2037146 · Cited by 259
Philip, S., Fleming, A. D., Goatman, K. A., Fonseca, S., Mcnamee, P., Scotland, G. S., Prescott, G. J., Sharp, P. F., & Olson, J. A. (2007) · British Journal of Ophthalmology · doi:10.1136/bjo.2007.119453 · Cited by 225
Combining algorithms for automatic detection of optic disc and macula in fundus images
Qureshi, R. J., Kovács, L., Harangi, B., Nagy, B., Pető, T., & Hajdú, A. (2011) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2011.09.001 · Cited by 138
Eom, J.-H., Kim, S., & ZHANG, B. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.04.015 · Cited by 128
Cited by
Leveraging uncertainty information from deep neural networks for disease detection
Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-17876-z · Cited by 510
A comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG)
Ayyub, A., Politis, C., & Usman, M. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110594 · Cited by 18
Metadata
- Authors
- Antal, B., & Hajdu, A.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2014-04
- Volume
- 60
- Pages
- 20-27
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2088422930
- Cited by
- 256
- References
- 19
- FWCI
- 9.86
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
