Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning

We propose a new method for the analysis and classification of HSI images. The method uses deep learning to interpret the molecular vibrational behaviour of healthy and tumoral human epithelial tissue, based on data gathered via SWIR (short-wave infrared) spectroscopy. We analyzed samples of Melanom...

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Detalles Bibliográficos
Autores principales: de Lucena, Daniel Vitor, da Silva Soares, Anderson, Coelho, Clarimar José, Wastowski, Isabela Jubé, Filho, Arlindo Rodrigues Galvão
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304037/
http://dx.doi.org/10.1007/978-3-030-50420-5_45
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author de Lucena, Daniel Vitor
da Silva Soares, Anderson
Coelho, Clarimar José
Wastowski, Isabela Jubé
Filho, Arlindo Rodrigues Galvão
author_facet de Lucena, Daniel Vitor
da Silva Soares, Anderson
Coelho, Clarimar José
Wastowski, Isabela Jubé
Filho, Arlindo Rodrigues Galvão
author_sort de Lucena, Daniel Vitor
collection PubMed
description We propose a new method for the analysis and classification of HSI images. The method uses deep learning to interpret the molecular vibrational behaviour of healthy and tumoral human epithelial tissue, based on data gathered via SWIR (short-wave infrared) spectroscopy. We analyzed samples of Melanoma, Dysplastic Nevus and healthy skin. Preliminary results show that human epithelial tissue is sensitive to SWIR to the point of making possible the differentiation between healthy and tumor tissues. We conclude that HSI-SWIR can be used to build new methods for tumor classification.
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spelling pubmed-73040372020-06-19 Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning de Lucena, Daniel Vitor da Silva Soares, Anderson Coelho, Clarimar José Wastowski, Isabela Jubé Filho, Arlindo Rodrigues Galvão Computational Science – ICCS 2020 Article We propose a new method for the analysis and classification of HSI images. The method uses deep learning to interpret the molecular vibrational behaviour of healthy and tumoral human epithelial tissue, based on data gathered via SWIR (short-wave infrared) spectroscopy. We analyzed samples of Melanoma, Dysplastic Nevus and healthy skin. Preliminary results show that human epithelial tissue is sensitive to SWIR to the point of making possible the differentiation between healthy and tumor tissues. We conclude that HSI-SWIR can be used to build new methods for tumor classification. 2020-05-22 /pmc/articles/PMC7304037/ http://dx.doi.org/10.1007/978-3-030-50420-5_45 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
de Lucena, Daniel Vitor
da Silva Soares, Anderson
Coelho, Clarimar José
Wastowski, Isabela Jubé
Filho, Arlindo Rodrigues Galvão
Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning
title Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning
title_full Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning
title_fullStr Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning
title_full_unstemmed Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning
title_short Detection of Tumoral Epithelial Lesions Using Hyperspectral Imaging and Deep Learning
title_sort detection of tumoral epithelial lesions using hyperspectral imaging and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304037/
http://dx.doi.org/10.1007/978-3-030-50420-5_45
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