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http://dx.doi.org/10.30693/SMJ.2021.10.4.45

Skin Disease Classification Technique Based on Convolutional Neural Network Using Deep Metric Learning  

Kim, Kang Min (조선대학교 컴퓨터공학과)
Kim, Pan-Koo (조선대학교 컴퓨터공학과)
Chun, Chanjun (조선대학교 컴퓨터공학과)
Publication Information
Smart Media Journal / v.10, no.4, 2021 , pp. 45-54 More about this Journal
Abstract
The skin is the body's first line of defense against external infection. When a skin disease strikes, the skin's protective role is compromised, necessitating quick diagnosis and treatment. Recently, as artificial intelligence has advanced, research for technical applications has been done in a variety of sectors, including dermatology, to reduce the rate of misdiagnosis and obtain quick treatment using artificial intelligence. Although previous studies have diagnosed skin diseases with low incidence, this paper proposes a method to classify common illnesses such as warts and corns using a convolutional neural network. The data set used consists of 3 classes and 2,515 images, but there is a problem of lack of training data and class imbalance. We analyzed the performance using a deep metric loss function and a cross-entropy loss function to train the model. When comparing that in terms of accuracy, recall, F1 score, and accuracy, the former performed better.
Keywords
convolutional neural network; deep metric loss; class imbalance; skin disease;
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