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Acral melanoma detection using a convolutional neural network for dermoscopy images
BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5841780/ https://www.ncbi.nlm.nih.gov/pubmed/29513718 http://dx.doi.org/10.1371/journal.pone.0193321 |
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author | Yu, Chanki Yang, Sejung Kim, Wonoh Jung, Jinwoong Chung, Kee-Yang Lee, Sang Wook Oh, Byungho |
author_facet | Yu, Chanki Yang, Sejung Kim, Wonoh Jung, Jinwoong Chung, Kee-Yang Lee, Sang Wook Oh, Byungho |
author_sort | Yu, Chanki |
collection | PubMed |
description | BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. METHODS: A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist’s and non-expert’s evaluation. RESULTS: The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert’s evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden’s index like 0.6795, 0.6073, which were similar score with the expert. CONCLUSION: Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet. |
format | Online Article Text |
id | pubmed-5841780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-58417802018-03-23 Acral melanoma detection using a convolutional neural network for dermoscopy images Yu, Chanki Yang, Sejung Kim, Wonoh Jung, Jinwoong Chung, Kee-Yang Lee, Sang Wook Oh, Byungho PLoS One Research Article BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. METHODS: A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist’s and non-expert’s evaluation. RESULTS: The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert’s evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden’s index like 0.6795, 0.6073, which were similar score with the expert. CONCLUSION: Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet. Public Library of Science 2018-03-07 /pmc/articles/PMC5841780/ /pubmed/29513718 http://dx.doi.org/10.1371/journal.pone.0193321 Text en © 2018 Yu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Yu, Chanki Yang, Sejung Kim, Wonoh Jung, Jinwoong Chung, Kee-Yang Lee, Sang Wook Oh, Byungho Acral melanoma detection using a convolutional neural network for dermoscopy images |
title | Acral melanoma detection using a convolutional neural network for dermoscopy images |
title_full | Acral melanoma detection using a convolutional neural network for dermoscopy images |
title_fullStr | Acral melanoma detection using a convolutional neural network for dermoscopy images |
title_full_unstemmed | Acral melanoma detection using a convolutional neural network for dermoscopy images |
title_short | Acral melanoma detection using a convolutional neural network for dermoscopy images |
title_sort | acral melanoma detection using a convolutional neural network for dermoscopy images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5841780/ https://www.ncbi.nlm.nih.gov/pubmed/29513718 http://dx.doi.org/10.1371/journal.pone.0193321 |
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