The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Automated melanoma recognition
Abstract
A system for the computerized analysis of images obtained from ELM has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%.
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
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,031
Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Wiley Series in Probability and Statistics
(2008) · Wiley series in probability and statistics · doi:10.1002/series1345 · Cited by 4,217
Floating search methods in feature selection
Pudil, P., Novovičová, J., & Kittler, J. (1994) · Pattern Recognition Letters · doi:10.1016/0167-8655(94)90127-9 · Cited by 3,098
Nachbar, F., Stolz, W., Merkle, T., Cognetta, A. B., Vogt, T., Landthaler, M., Bilek, P., Braun-Falco, O., & Plewig, G. (1994) · Journal of the American Academy of Dermatology · doi:10.1016/s0190-9622(94)70061-3 · Cited by 882
Computer graphics — principles and practice
Leitáo, V. (1992) · Advances in Engineering Software · doi:10.1016/0965-9978(92)90087-v · Cited by 820
Color image quantization for frame buffer display
Heckbert, P. (1982) · Proceedings of the 9th annual conference on Computer graphics and interactive techniques · doi:10.1145/800064.801294 · Cited by 656
The Image Processing Handbook, 2nd Ed
Russ, J. C., & Woods, R. P. (1995) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199511000-00026 · Cited by 644
Pehamberger, H., Steiner, A., & Wolff, K. (1987) · Journal of the American Academy of Dermatology · doi:10.1016/s0190-9622(87)70239-4 · Cited by 634
Cited by
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620
Self-citationAutomated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
Yu, L., Chen, H., Dou, Q., Qin, J., & Heng, P.-A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2642839 · Cited by 1,140
PH2 - A dermoscopic image database for research and benchmarking
Mendonca, T., Ferreira, P. M., Marques, J. S., Marcal, A. R. S., & Rozeira, J. (2013) · 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610779 · Cited by 1,009
A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
Padilla, R., Passos, W. L., Dias, T. L. B., Netto, S. L., & da Silva, E. A. B. (2021) · Electronics · doi:10.3390/electronics10030279 · Cited by 735
Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance.
Yuan, Y., Chao, M., & Lo, Y.-C. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2695227 · Cited by 687
Metadata
- Authors
- Ganster, H., Pinz, P., Rohrer, R., Wildling, E., Binder, M., & Kittler, H.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2001-03
- Volume
- 20
- Issue
- 3
- Pages
- 233-239
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2162515926
- Cited by
- 631
- References
- 20
- FWCI
- 4.61
- Type
- journal article
- 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: Automated melanoma recognition. https://app.cochat.ai/papers/10.1109/42.918473
- The paper
- Ganster, H., Pinz, P., Rohrer, R., Wildling, E., Binder, M., & Kittler, H. (2001). Automated melanoma recognition. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/42.918473
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.
