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dc.contributor.authorJohansen, Thomas Haugland
dc.contributor.authorMøllersen, Kajsa
dc.contributor.authorOrtega, Samuel
dc.contributor.authorFabelo, Himar
dc.contributor.authorGarcia, Aday
dc.contributor.authorCallico, Gustavo
dc.contributor.authorGodtliebsen, Fred
dc.date.accessioned2019-11-13T14:29:24Z
dc.date.available2019-11-13T14:29:24Z
dc.date.issued2019-04-22
dc.description.abstractSkin cancer is one of the most common types of cancer. Skin cancers are classified as nonmelanoma and melanoma, with the first type being the most frequent and the second type being the most deadly. The key to effective treatment of skin cancer is early detection. With the recent increase of computational power, the number of algorithms to detect and classify skin lesions has increased. The overall verdict on systems based on clinical and dermoscopic images captured with conventional RGB (red, green, and blue) cameras is that they do not outperform dermatologists. Computer‐based systems based on conventional RGB images seem to have reached an upper limit in their performance, while emerging technologies such as hyperspectral and multispectral imaging might possibly improve the results. These types of images can explore spectral regions beyond the human eye capabilities. Feature selection and dimensionality reduction are crucial parts of extracting salient information from this type of data. It is necessary to extend current classification methodologies to use all of the spatiospectral information, and deep learning models should be explored since they are capable of learning robust feature detectors from data. There is a lack of large, high‐quality datasets of hyperspectral skin lesion images, and there is a need for tools that can aid with monitoring the evolution of skin lesions over time. To understand the rich information contained in hyperspectral images, further research using data science and statistical methodologies, such as functional data analysis, scale‐space theory, machine learning, and so on, are essential.en_US
dc.descriptionThis is the peer reviewed version of the following article: Johansen, T.H., Møllersen, K., Ortega, S., Fabelo, H., Garcia, A., Callico, G.M. & Godtliebsen, F. (2019). Recent advances in hyperspectral imaging for melanoma detection. <i>Wiley Interdisciplinary Reviews: Computational Statistics</i>, e1465, which has been published in final form at <a href=https://doi.org/10.1002/wics.1465>https://doi.org/10.1002/wics.1465</a>. This article may be used for non-commercial purposes in accordance with Wiley <a href=https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html>Terms and Conditions for Use of Self-Archived Versions</a>.en_US
dc.identifier.citationJohansen, T.H., Møllersen, K., Ortega, S., Fabelo, H., Garcia, A., Callico, G.M. & Godtliebsen, F. (2019). Recent advances in hyperspectral imaging for melanoma detection. <i>Wiley Interdisciplinary Reviews: Computational Statistics</i>, e1465. https://doi.org/10.1002/wics.1465en_US
dc.identifier.cristinIDFRIDAID 1693386
dc.identifier.doi10.1002/wics.1465
dc.identifier.issn1939-5108
dc.identifier.issn1939-0068
dc.identifier.urihttps://hdl.handle.net/10037/16661
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.ispartofJohansen, T.H. (2021). Leveraging Computer Vision for Applications in Biomedicine and Geoscience. (Doctoral thesis). <a href=https://hdl.handle.net/10037/21377>https://hdl.handle.net/10037/21377</a>.
dc.relation.journalWiley Interdisciplinary Reviews: Computational Statistics
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Medical disciplines: 700::Clinical medical disciplines: 750::Oncology: 762en_US
dc.subjectVDP::Medisinske Fag: 700::Klinisk medisinske fag: 750::Onkologi: 762en_US
dc.subjectVDP::Technology: 500::Medical technology: 620en_US
dc.subjectVDP::Teknologi: 500::Medisinsk teknologi: 620en_US
dc.titleRecent advances in hyperspectral imaging for melanoma detectionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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