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KMID : 0363220180560070421
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Korean Journal of Dermatology
2018 Volume.56 No. 7 p.421 ~ p.425
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Acne Severity Scoring Using Deep Learning
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Lee Suk-Jun
Jeon Jong-Soo
Ryu Jae-Yeon
Song Hyun-Jeong
Jo Yoon-Jae
Bang Chul-Hwan
Park Young-Min
Lee Ji-Hyun
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Abstract
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Background: Acne is a chronic inflammatory disease of the pilosebaceous unit, mainly on the face. It can have various clinical manifestations and should be appropriately treated based on the severity. In Korea, the ’Korea Acne Severity Rating System (KAGS)’ is a standardized index to determine the severity of acne according to specific Korean characteristics. However, the actual use of the KAGS in clinical settings has been limited.
Objective: We sought to analyze whether we could effectively measure acne severity using a deep learning algorithm, which is an image learning method.
Methods: Acne severity was classified into three levels of mild, moderate, and severe based on the KAGS, and learning and verification were performed using the CNN (Convolutional Neural Network), a deep learning technique.
Results: GoogLeNet’s Inception-v3 algorithm showed the highest accuracy at 86.7%.
Conclusion: This study confirmed that the use of a deep learning algorithm may facilitate the scoring of acne severity
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KEYWORD
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Acne severity, Convolutional Neural Network Korean acne grading system, Deep learning
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