The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities
Open accessAbstract
Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in this domain. A large number of skin lesion datasets are available publicly, and researchers have developed AI solutions, particularly deep learning algorithms, to distinguish malignant skin lesions from benign lesions in different image modalities such as dermoscopic, clinical, and histopathology images. Despite the various claims of AI systems achieving higher accuracy than dermatologists in the classification of different skin lesions, these AI systems are still in the very early stages of clinical application in terms of being ready to aid clinicians in the diagnosis of skin cancers. In this review, we discuss advancements in the digital image-based AI solutions for the diagnosis of skin cancer, along with some challenges and future opportunities to improve these AI systems to support dermatologists and enhance their ability to diagnose skin cancer.
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Cited by
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Metadata
- Authors
- Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 127
- Pages
- 104065
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3097337942
- Cited by
- 428
- References
- 65
- FWCI
- 16.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by/4.0/ (vor)
- Funders
- National Institutes of Health (R01CA249758, R01LM012837)
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- Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020). Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2020.104065
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