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http://dx.doi.org/10.3745/KTSDE.2021.10.12.555

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

Lim, Chae Hyun (숭실대학교 소프트웨어학과)
Son, Min Ji (숭실대학교 융합소프트웨어학과)
Kim, Myung Ho (숭실대학교 소프트웨어학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.12, 2021 , pp. 555-560 More about this Journal
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.
Keywords
Deep Learning; Multi-Label Classification; Skin Diseases;
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