Automated grading of acne vulgaris by deep learning with convolutional neural networks-Reference-Cited by-同舟云学术

Automated grading of acne vulgaris by deep learning with convolutional neural networks

Published:2019-09-29 Issue:2 Volume:26 Page:187-192
ISSN:0909-752X
Container-title:Skin Research and Technology
language:en
Short-container-title:Skin Res Technol

Author:

Lim Ziying Vanessa¹^ORCID,Akram Farhan²,Ngo Cuong Phuc²³,Winarto Amadeus Aristo²³,Lee Wei Qing⁴,Liang Kaicheng²,Oon Hazel Hweeboon¹^ORCID,Thng Steven Tien Guan¹⁵,Lee Hwee Kuan²⁴⁶⁷

Affiliation:

1. National Skin Centre Singapore Singapore

2. Bioinformatics Institute Agency for Science, Technology and Research (A*STAR) Singapore Singapore

3. Hwa Chong Institution Singapore Singapore

4. School of Computing National University of Singapore Singapore Singapore

5. Skin Research Institute Singapore A*STAR Singapore Singapore

6. Image and Pervasive Access Lab CNRS Singapore Singapore

7. Singapore Eye Research Institute Singapore Singapore

Publisher

Wiley

Subject

Dermatology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/srt.12794

Reference18 articles.

1. A global perspective on the epidemiology of acne

2. Establishment of grading criteria for acne severity

3. Research C. for D. E. and. Acne vulgaris: establishing effectiveness of drugs intended for treatment. U.S. Food and Drug Administration. Available at:http://www.fda.gov/regulatory-information/search-fda-guidance-documents/acne-vulgaris-establishing-effectiveness-drugs-intended-treatment. Accessed June 20 2019.

4. Objective assessment of acne

5. Inter-observer agreement on acne severity based on facial photographs

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