Design of a multilayer neural network for the classification of skin ulcers’ hyperspectral images: a proof of concept

Jaramillo, Daniel; Escobar, John E.; Galeano, July; Torres-Madronero, Maria C.

doi:10.1117/12.2542277

Cited by 2 publications

(1 citation statement)

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“…The cameras had different resolutions, so they were registered by mutual information maximization. Carmona Jaramillo et al 63 analyzed images from research prototype IMPULSO, but did not provide additional information regarding system design. Zherdeva et al 30 used an experimental setup comprised of a tunable monochromator and broadband LED.…”

Section: Hyperspectral Prototype Systemsmentioning

confidence: 99%

Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review

et al. 2022

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Significance: Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level.Aim: We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.Approach: A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified.Results: HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems.Conclusions: To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

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Section: Hyperspectral Prototype Systemsmentioning

confidence: 99%