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Acral melanoma detection using a convolutional neural network for dermoscopy images

Authors
 Chanki Yu  ;  Sejung Yang  ;  Wonoh Kim  ;  Jinwoong Jung  ;  Kee-Yang Chung  ;  Sang Wook Lee  ;  Byungho Oh 
Citation
 PLoS One, Vol.13(3) : e0193321, 2018 
Journal Title
 PLoS One 
Issue Date
2018
MeSH
Dermoscopy/*methods ; Early Detection of Cancer/methods ; Foot/diagnostic imaging/pathology ; Hand/diagnostic imaging/pathology ; Humans ; Computer-Assisted/*methods Image Interpretation ; Melanoma/*diagnostic imaging/pathology ; *Neural Networks (Computer) ; Sensitivity and Specificity ; Skin/*diagnostic imaging/pathology ; Skin Neoplasms/*diagnostic imaging/pathology
Abstract
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.
Files in This Item:
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DOI
10.1371/journal.pone.0193321
Appears in Collections:
1. Journal Papers (연구논문) > 1. College of Medicine (의과대학) > Dept. of Dermatology (피부과학교실)
Yonsei Authors
오병호(Oh, Byung Ho) ORCID logo https://orcid.org/0000-0001-9575-5665
정기양(Chung, Kee Yang)
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URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/162304
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