Recent advances in hyperspectral imaging for melanoma detection. (22nd April 2019)

• Record Type: Journal Article
• Authors: Johansen, Thomas Haugland; Møllersen, Kajsa; Ortega, Samuel; Fabelo, Himar; Garcia, Aday; Callico, Gustavo M.; Godtliebsen, Fred
• Is Part Of: Wiley interdisciplinary reviews. Volume 12:Number 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-22
• Subjects: hyperspectral -- machine learning -- melanoma -- skin cancer; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1465
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract: Skin 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. This article is categorized under: Applications of Computational Statistics > Health and Medical Data/Informatics Abstract : Transitioning computer‐aided melanoma detection systems from conventional RGB images to hyperspectral images will not only provide richer information, but also introduces the possibility of exploiting spectral regions outside the visible light spectrum