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Cited by 211

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

Barata, Catarina, Celebi, M. Emre, Marques, Jorge S.·2019·IEEE Journal of Biomedical and Health Informatics

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.

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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

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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

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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.