A Convolutional Neural Network for Lentigo Diagnosis

Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect...

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Autores principales: Zorgui, Sana, Chaabene, Siwar, Bouaziz, Bassem, Batatia, Hadj, Chaari, Lotfi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313289/
http://dx.doi.org/10.1007/978-3-030-51517-1_8
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author Zorgui, Sana
Chaabene, Siwar
Bouaziz, Bassem
Batatia, Hadj
Chaari, Lotfi
author_facet Zorgui, Sana
Chaabene, Siwar
Bouaziz, Bassem
Batatia, Hadj
Chaari, Lotfi
author_sort Zorgui, Sana
collection PubMed
description Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy.
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spelling pubmed-73132892020-06-24 A Convolutional Neural Network for Lentigo Diagnosis Zorgui, Sana Chaabene, Siwar Bouaziz, Bassem Batatia, Hadj Chaari, Lotfi The Impact of Digital Technologies on Public Health in Developed and Developing Countries Article Using Reflectance Confocal Microscopy (RCM) for lentigo diagnosis is today considered essential. Indeed, RCM allows fast data acquisition with a high spatial resolution of the skin. In this paper, we use a deep convolutional neural network (CNN) to perform RCM image classification in order to detect lentigo. The proposed method relies on an InceptionV3 architecture combined with data augmentation and transfer learning. The method is validated on RCM data and shows very efficient detection performance with more than 98% of accuracy. 2020-05-31 /pmc/articles/PMC7313289/ http://dx.doi.org/10.1007/978-3-030-51517-1_8 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
spellingShingle Article
Zorgui, Sana
Chaabene, Siwar
Bouaziz, Bassem
Batatia, Hadj
Chaari, Lotfi
A Convolutional Neural Network for Lentigo Diagnosis
title A Convolutional Neural Network for Lentigo Diagnosis
title_full A Convolutional Neural Network for Lentigo Diagnosis
title_fullStr A Convolutional Neural Network for Lentigo Diagnosis
title_full_unstemmed A Convolutional Neural Network for Lentigo Diagnosis
title_short A Convolutional Neural Network for Lentigo Diagnosis
title_sort convolutional neural network for lentigo diagnosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313289/
http://dx.doi.org/10.1007/978-3-030-51517-1_8
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