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Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images

Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted ligh...

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Autores principales: La Salvia, Marco, Torti, Emanuele, Leon, Raquel, Fabelo, Himar, Ortega, Samuel, Balea-Fernandez, Francisco, Martinez-Vega, Beatriz, Castaño, Irene, Almeida, Pablo, Carretero, Gregorio, Hernandez, Javier A., Callico, Gustavo M., Leporati, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571453/
https://www.ncbi.nlm.nih.gov/pubmed/36236240
http://dx.doi.org/10.3390/s22197139
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author La Salvia, Marco
Torti, Emanuele
Leon, Raquel
Fabelo, Himar
Ortega, Samuel
Balea-Fernandez, Francisco
Martinez-Vega, Beatriz
Castaño, Irene
Almeida, Pablo
Carretero, Gregorio
Hernandez, Javier A.
Callico, Gustavo M.
Leporati, Francesco
author_facet La Salvia, Marco
Torti, Emanuele
Leon, Raquel
Fabelo, Himar
Ortega, Samuel
Balea-Fernandez, Francisco
Martinez-Vega, Beatriz
Castaño, Irene
Almeida, Pablo
Carretero, Gregorio
Hernandez, Javier A.
Callico, Gustavo M.
Leporati, Francesco
author_sort La Salvia, Marco
collection PubMed
description Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints.
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spelling pubmed-95714532022-10-17 Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images La Salvia, Marco Torti, Emanuele Leon, Raquel Fabelo, Himar Ortega, Samuel Balea-Fernandez, Francisco Martinez-Vega, Beatriz Castaño, Irene Almeida, Pablo Carretero, Gregorio Hernandez, Javier A. Callico, Gustavo M. Leporati, Francesco Sensors (Basel) Article Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints. MDPI 2022-09-21 /pmc/articles/PMC9571453/ /pubmed/36236240 http://dx.doi.org/10.3390/s22197139 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
La Salvia, Marco
Torti, Emanuele
Leon, Raquel
Fabelo, Himar
Ortega, Samuel
Balea-Fernandez, Francisco
Martinez-Vega, Beatriz
Castaño, Irene
Almeida, Pablo
Carretero, Gregorio
Hernandez, Javier A.
Callico, Gustavo M.
Leporati, Francesco
Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images
title Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images
title_full Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images
title_fullStr Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images
title_full_unstemmed Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images
title_short Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images
title_sort neural networks-based on-site dermatologic diagnosis through hyperspectral epidermal images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571453/
https://www.ncbi.nlm.nih.gov/pubmed/36236240
http://dx.doi.org/10.3390/s22197139
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