A Study on Facial Skin Disease Recognition Using Multi-Label Classification


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 12, pp. 555-560, Dec. 2021
https://doi.org/10.3745/KTSDE.2021.10.12.555,   PDF Download:
Keywords: Deep Learning, Multi-label classification, Skin Diseases
Abstract

Recently, as people's interest in facial skin beauty has increased, research on skin disease recognition for facial skin beauty is being conducted by using deep learning. These studies recognized a variety of skin diseases, including acne. Existing studies can recognize only the single skin diseases, but skin diseases that occur on the face can enact in a more diverse and complex manner. Therefore, in this paper, complex skin diseases such as acne, blackheads, freckles, age spots, normal skin, and whiteheads are identified using the Inception-ResNet V2 deep learning mode with multi-label classification. The accuracy was 98.8%, hamming loss was 0.003, and precision, recall, F1-Score achieved 96.6% or more for each single class.


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Cite this article
[IEEE Style]
C. H. Lim, S. M. Ji, K. M. Ho, "A Study on Facial Skin Disease Recognition Using Multi-Label Classification," KIPS Transactions on Software and Data Engineering, vol. 10, no. 12, pp. 555-560, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.12.555.

[ACM Style]
Chae Hyun Lim, Son Min Ji, and Kim Myung Ho. 2021. A Study on Facial Skin Disease Recognition Using Multi-Label Classification. KIPS Transactions on Software and Data Engineering, 10, 12, (2021), 555-560. DOI: https://doi.org/10.3745/KTSDE.2021.10.12.555.