• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Korean Journal of Optics and Photonics
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• v.22 no.1
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• pp.46-52
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• 2011

A confocal microscope was developed utilizing a scanning sample stage based on a home-built double-compound flexure guide. A scanning sample stage with nano-scale resolution consisted of a double leaf spring based flexure, a displacement amplifying lever, a Piezo-electric Transducer(PZT) actuator and capacitance sensors. The performance of the two-axis stage was analyzed using a commercial finite element method program prior to the implementation. A single line laser was employed as the light source along with the Photo Multiplier Tube(PMT) that served as the detector. The performance of the developed confocal microscope was evaluated with a mouse ear skin imaging test. The designed scanning stage enabled us to build the confocal microscope without the two optical scanning mirror modules that are essential in the conventional laser scanning confocal microscope. The elimination of the scanning mirror modules makes the optical design of the confocal microscope simpler and more compact than the conventional system.