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Recent advances in hyperspectral imaging for melanoma detection

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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
Examples of melanoma and nonmelanoma skin cancer taken in a clinical setting from six different patients. These cases represent both nonmelanoma and melanoma skin cancer. The diagnoses of the lesions based on histopathology are as follows: (1) melanoma, (2) atypical melanocytic hyperplasia, (3) squamous cell carcinoma, (4) Bowen's disease, (5) basal cell carcinoma, and (6) seborrheic keratosis
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Examples of the different information captured in hyperspectral images at different wavelengths. Each image represents a reflectance image at a specific wavelength. Note how certain features appear and disappear at the various wavelengths. In particular, note how the small lesion visible near the right‐most edge of the left image is almost invisible at higher wavelengths, and at higher wavelengths, smaller subregions and structures in the central lesion become visible
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An example of what spectral curves for hyperspectral pixels can look like. The plot on the left shows a representation of a hyperspectral reflectance image at an arbitrarily chosen wavelength. On the right, the mean reflectance values are plotted, where the colors of the curves correspond to the colored regions in the reflectance image. The mean curves are calculated based on all pixels in each region
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The number of publications per year that matched our search queries and were selected for review based on our inclusion criteria. From the plot, we can see that the majority of reviewed publications were published after 2011
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The conceptual difference between the information richness in a hyperspectral cube and an RGB image. In the hyperspectral cube, each horizontal slice represents spatial response for a discrete wavelength. For the RGB image, each slice represents spatial information across a range of wavelengths. Each of the red, green, and blue slices are calculated based on the visual light spectrum associated with each respective color
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The image on the left is an example of a conventional, clinical image of a pigmented skin lesion, whereas the image on the right is an example of dermoscopic image
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