An Experimental Comparison of CNN-based Deep Learning Algorithms for Recognition of Beauty-related Skin Disease (미용관련 피부질환 인식을 위한 CNN기반 딥러닝 알고리즘의 실험적 비교)

Chang-Hui Bae (배창희); Won-Young Cho (조원영); Hyeong-Jun Kim (김형준); Ok-Kyoon Ha (하옥균)

doi:10.9708/jksci.2020.25.12.025

An Experimental Comparison of CNN-based Deep Learning Algorithms for Recognition of Beauty-related Skin Disease

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(12), pp.25-34
DOI : 10.9708/jksci.2020.25.12.025
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : October 14, 2020
Accepted : November 19, 2020
Published : December 31, 2020

Chang-Hui Bae ¹, Won-Young Cho ¹, Hyeong-Jun Kim ¹, Ok-Kyoon Ha ¹

¹경운대학교

Accredited

ABSTRACT

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.

KEYWORDS

Deep Learning, CNN, Beauty-related Skin Disease Recognition, Image Recognition, Algorithm Comparison, Experimental Comparison

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

An Experimental Comparison of CNN-based Deep Learning Algorithms for Recognition of Beauty-related Skin Disease

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

An Experimental Comparison of CNN-based Deep Learning Algorithms for Recognition of Beauty-related Skin Disease

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