The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
the DIARETDB1 diabetic retinopathy database and evaluation protocol
Abstract
Automatic diagnosis of diabetic retinopathy from digital fundus images has been an active research topic in the medical image processing community. The research interest is justified by the excellent potential for new products in the medical industry and significant reductions in health care costs. However, the maturity of proposed algorithms cannot be judged due to the lack of commonly accepted and representative image database with a verified ground truth and strict evaluation protocol. In this study, an evaluation methodology is proposed and an image database with ground truth is described. The database is publicly available for benchmarking diagnosis algorithms. With the proposed database and protocol, it is possible to compare different algorithms, and correspondingly, analyse their maturity for technology transfer from the research laboratories to the medical practice.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Wilkinson, C. P., Ferris, F. L., Klein, R. E., Lee, P. P., Agardh, C. D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., & Verdaguer, J. T. (2003) · Ophthalmology · doi:10.1016/s0161-6420(03)00475-5 · Cited by 3,743
The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
The PASCAL visual object classes challenge 2006 (VOC2006) results
Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079
Image change detection algorithms: a systematic survey
Radke, R. J., Andra, S., Al-Kofahi, O., & Roysam, B. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.838698 · Cited by 1,869
Walter, T., Klein, J., Massin, P., & Erginay, A. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.806290 · Cited by 825
The use of receiver operating characteristic curves in biomedical informatics
Lasko, T. A., Bhagwat, J. G., Zou, K. H., & Ohno-Machado, L. (2005) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2005.02.008 · Cited by 823
Automated Feature Extraction in Color Retinal Images by a Model Based Approach
Li, H., & Chutatape, O. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.820400 · Cited by 545
Automated detection of diabetic retinopathy on digital fundus images
Sinthanayothin, C., Boyce, J. F., Williamson, T. H., Cook, H. L., Mensah, E., Lal, S., & Usher, D. (2002) · Diabetic Medicine · doi:10.1046/j.1464-5491.2002.00613.x · Cited by 509
Automatic detection of red lesions in digital color fundus photographs
Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M. S. A., & Abramoff, M. D. (2005) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.843738 · Cited by 504
The BANCA Database and Evaluation Protocol
Bailly-Bailliére, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Mariéthoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., et al. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-44887-x_74 · Cited by 423
Cited by
RIM-ONE: An open retinal image database for optic nerve evaluation
Fumero, F., Alayon, S., Sanchez, J. L., Sigut, J., & Gonzalez-Hernandez, M. (2011) · 2011 24th International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms.2011.5999143 · Cited by 538
(2018) · doi:10.1117/12.2293584 · Cited by 171
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
Wagner, J., Köhler, J. M., Gindele, T., Hetzel, L., Wiedemer, J. T., & Behnke, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00931 · Cited by 144
Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy
Huang, X., Wang, H., She, C., Feng, J., Liu, X., Hu, X., Chen, L., & Tao, Y. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.946915 · Cited by 115
Elsharkawy, M., Elrazzaz, M., Sharafeldeen, A., Alhalabi, M., Khalifa, F., Soliman, A., Elnakib, A., Mahmoud, A., Ghazal, M., El-Daydamony, E., et al. (2022) · Sensors · doi:10.3390/s22093490 · Cited by 38
Metadata
- Authors
- Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kalviainen, H., & Pietila, J.
- Venue
- Procedings of the British Machine Vision Conference 2007
- Publisher
- British Machine Vision Association
- Published
- 2007
- Pages
- 15.1-15.10
- OpenAlex
- W1091878006
- Cited by
- 854
- References
- 15
- FWCI
- 27.96
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: the DIARETDB1 diabetic retinopathy database and evaluation protocol. https://app.cochat.ai/papers/10.5244/c.21.15
- The paper
- Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kalviainen, H., & Pietila, J. (2007). the DIARETDB1 diabetic retinopathy database and evaluation protocol. Procedings of the British Machine Vision Conference 2007. https://doi.org/10.5244/c.21.15
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
