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http://dx.doi.org/10.9723/jksiis.2021.26.2.001

Abnormal Detection with Microscope through Deep Learning  

Jeong, Hieyong (전남대학교 AI융합대학 IoT인공지능융합전공)
Go, Jeongwon (전남대학교 AI융합대학 IoT인공지능융합전공)
Shin, Choonsung (전남대학교 문화전문대학원 미디어예술공학전공)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.26, no.2, 2021 , pp. 1-10 More about this Journal
Abstract
The success rate of the no-smoking campaign has been low, although everybody knows that cigarettes are harmful to the human health. The results of both regular health and cancer checks in the hospital are useful for strengthening the human intention for quitting the smoking, however, those methods are difficult to use in daily life because of the use of large-scaled particular devices such as PET(positron emission tomography). Thus, this study proposed a non-invasive method that detects the difference between smokers and non-smokers through deep-learning-based analysis. At first, observing parts were decided to the tongue surface. Then, a data set(total 1,000) was made through the experiment to measure the tongue surface(410 times magnification) with the participants of 10 smokers and 10 non-smokers. The 80% ratio of data set was used for the train, and the left 20% was for the prediction. As a result, it was found that the classification through EfficientNet with the compound scaling including three scaling methods of width scaling, depth scaling and resolution scaling was much better than other models including VGG, ResNet, and DenseNet with the only one scaling.
Keywords
abnormal detection; deep learning; microscope; tongue surface; smoke;
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