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http://dx.doi.org/10.9708/jksci.2020.25.12.025

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

Bae, Chang-Hui (Dept. of Aeronautical Software Engineering, Kyungwoon University)
Cho, Won-Young (Dept. of Aeronautical Software Engineering, Kyungwoon University)
Kim, Hyeong-Jun (Dept. of Aeronautical Software Engineering, Kyungwoon University)
Ha, Ok-Kyoon (Dept. of Aeronautical Software Engineering, Kyungwoon University)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.12, 2020 , pp. 25-34 More about this Journal
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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