The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer
Abstract
Dermoscopy image analysis (DIA) is a growing field, with works being published every week. This makes it difficult not only to keep track of all the contributions, but also for new researchers to identify relevant information and new directions to be explored. Several surveys have been written in the past decade, but these tend to cover all of the steps of a CAD system, which can be overwhelming. Moreover, in these works, each of the steps is briefly discussed due to lack of space. Among the different blocks of the CAD system, the most relevant is the one devoted to feature extraction. This is also the block where existing works exhibit the most variability. Therefore, we believe that it is important to review the state-of-the-art on this matter. This work thoroughly explores the several types of features that have been used in DIA. A discussion on their relevance and limitations, as well as suggestions for future research are provided.
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
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-citationDeep learning for visual understanding: A review
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504
Incidence Estimate of Nonmelanoma Skin Cancer (Keratinocyte Carcinomas) in the US Population, 2012
Rogers, H. W., Weinstock, M. A., Feldman, S. R., & Coldiron, B. M. (2015) · JAMA Dermatology · doi:10.1001/jamadermatol.2015.1187 · Cited by 1,815
Image Classification with the Fisher Vector: Theory and Practice
Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498
Cited by
Classification of skin lesions using transfer learning and augmentation with Alex-net
Hosny, K. M., Kassem, M. A., & Foaud, M. M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217293 · Cited by 373
Saba, T. (2020) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2020.06.033 · Cited by 284
Khan, M. A., Sharif, M., Akram, T., Damaševičius, R., & Maskeliūnas, R. (2021) · Diagnostics · doi:10.3390/diagnostics11050811 · Cited by 281
Background selection schema on deep learning-based classification of dermatological disease
Zhou, J., Wu, Z., Jiang, Z., Huang, K., Guo, K., & Zhao, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105966 · Cited by 53
Boosting skin cancer diagnosis accuracy with ensemble approach
Natha, P., Tera, S. P., Chinthaginjala, R., Rab, S. O., Narasimhulu, C. V., & Kim, T. H. (2025) · Scientific Reports · doi:10.1038/s41598-024-84864-5 · Cited by 45
Metadata
- Authors
- Barata, C., Celebi, M. E., & Marques, J. S.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2019-05
- Volume
- 23
- Issue
- 3
- Pages
- 1096-1109
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2808402517
- Cited by
- 211
- References
- 13
- FWCI
- 6.24
- 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: A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer. https://app.cochat.ai/papers/10.1109/jbhi.2018.2845939
- The paper
- Barata, C., Celebi, M. E., & Marques, J. S. (2019). A Survey of Feature Extraction in Dermoscopy Image Analysis of Skin Cancer. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2018.2845939
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.