Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Mar 7;13(3):e0193321.
doi: 10.1371/journal.pone.0193321. eCollection 2018.

Acral melanoma detection using a convolutional neural network for dermoscopy images

Chanki Yu  1 , Sejung Yang  2   3 , Wonoh Kim  4 , Jinwoong Jung  4 , Kee-Yang Chung  5 , Sang Wook Lee  1 , Byungho Oh  4
Affiliations

Acral melanoma detection using a convolutional neural network for dermoscopy images

Chanki Yu et al. PLoS One. .

Erratum in

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.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart of this study.
Fig 2
Fig 2. Schematic overview of our CNN architecture: The number of output classes was set to 2 (melanoma and non-melanoma classes) for the dermoscopic images.
Fig 3
Fig 3. The framework of the melanoma classification showing training (upper) and inference (lower) stages.
Fig 4
Fig 4. Visualization of the learned filters: (a) 64 learned filters at the first layer, (b-m) 100 filters among the learned filters from the 2nd to 13th layers, respectively.
Fig 5
Fig 5. Comparison of diagnostic reliability based on the area under the curve (AUC).
See this image and copyright information in PMC

References

    1. Roh MR, Kim J, Chung KY. Treatment and outcomes of melanoma in acral location in Korean patients. Yonsei Med J. 2010;51(4):562–8. Epub 2010/05/26. doi: 10.3349/ymj.2010.51.4.562 . - DOI - PMC - PubMed
    1. Franke W, Neumann NJ, Ruzicka T, Schulte KW. Plantar malignant melanoma—a challenge for early recognition. Melanoma Res. 2000;10(6):571–6. Epub 2001/02/24. . - PubMed
    1. Kato T, Suetake T, Sugiyama Y, Tanita Y, Kumasaka K, Takematsu H, et al. Improvement in survival rate of patients with acral melanoma observed in the past 22 years in Sendai, Japan. Clin Exp Dermatol. 1993;18(2):107–10. Epub 1993/03/01. . - PubMed
    1. Tran KT, Wright NA, Cockerell CJ. Biopsy of the pigmented lesion—when and how. J Am Acad Dermatol. 2008;59(5):852–71. Epub 2008/09/03. doi: 10.1016/j.jaad.2008.05.027 . - DOI - PubMed
    1. Argenziano G, Soyer HP, Chimenti S, Talamini R, Corona R, Sera F, et al. Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J Am Acad Dermatol. 2003;48(5):679–93. Epub 2003/05/08. doi: 10.1067/mjd.2003.281 . - DOI - PubMed

Publication types