• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.463-468
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• 2022

The prolonged of the Post Corona made many industry's paradigm. It's become very important In the industries products that customers directly touch and use. To cope with this situation, The Cosmetics industry has recently introduced various untact services. many customers would like to try these new services. Typically, online survey services recommend personalized products. but these services reached its limit later. This paper research how to recommend products and define skine type with AI Image diagnosis module combine with legacy survey system.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.33-34
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• 2020

사용자의 피부 관리 및 피부질환을 검사하는 기존의 어플리케이션은 유도 질문에 따른 사용자의 응답을 기반으로 결과를 유추하기 때문에 부정확한 진단 결과를 야기한다. 본 논문에서는 사용자의 미용관련 피부질환 이미지를 바탕으로 딥러닝 이미지 인식 기술 적용하여 건선, 사마귀, 여드름, 한포진을 대상으로 피부 미용질환에 대한 식별 정보를 제공하는 어플리케이션을 제시한다. 또한 이미지 인식률이 높은 ResNet과 SE-ResNet 알고리즘을 적용하여 피부질환 식별 적용 시 효과성을 실험적으로 비교한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.698-700
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• 2023

최근 들어, 미용 상품을 선택하기 전에 자신의 피부 타입과 상태를 정확히 파악하고 맞춤형 상품을 선택하고자 하는 수요가 증가하고 있다. 이에 따라 피부 상태 측정을 위한 기술적 요소의 중요성이 더욱 두드러지고 있다. 그러나 현재까지 피부 상태 측정을 위한 데이터셋이 한국인을 대상으로 측정한 데이터셋이 없는 실정이다. 본 연구에서는 한국인의 피부 상태를 정밀하게 분석하기 위해 고해상도 디지털 카메라로 촬영된 이미지, 정밀 피부측정 장비를 활용하여 측정한 정밀 값, 그리고 피부과 전문의가 진단한 피부상태 진단 등급 데이트를 통합하여 제공을 한다. 추후 제작한 데이터셋을 활용하여 개인 맞춤형 미용상품 추천과 개발 등 다양한 분야에 활용하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.958-961
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• 2014

최근 영상 분석을 기반으로 한 서비스 어플리케이션의 공급량이 증가하는 추세이다. 특히, 피부 영상 분석 서비스의 경우 주목할 만한 이슈는 접근의 용이함과 편리성을 갖춘 서비스 어플리케이션의 개발이다. 본 논문에서는 사용자의 피부 상태 진단 서비스를 손쉽게 웹 상으로 제공받을 수 있는 어플리케이션 개발에 주안점을 둔다. 이를 위해 피부 현미경으로 촬영된 이미지에 이진화 및 질감 대비 향상, 노이즈 제거 등의 전처리 과정과 Watershed 알고리즘, 외곽선 검출 등의 과정을 거쳐 수치화된 데이터를 산출한다. 최종적으로 피부 주름, 거칠기, 유분, 톤, 민감성 정보를 검출하며 분석 결과를 사용자에게 보여준다. 분석된 피부 영상 정보를 통해 사용자는 쉽게 자신의 피부 상태를 진단 받을 수 있을 것으로 사료된다.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.25-34
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• 2024

This paper presents the development of a mobile application that detects and identifies canine skin diseases by training a deep learning-based U-Net model to infer the presence and location of skin lesions from images. U-Net, primarily used in medical imaging for image segmentation, is effective in distinguishing specific regions of an image in a polygonal form, making it suitable for identifying lesion areas in dogs. In this study, six major canine skin diseases were defined as classes, and the U-Net model was trained to differentiate among them. The model was then implemented in a mobile app, allowing users to perform lesion analysis and prediction through simple camera shots, with the results provided directly to the user. This enables pet owners to monitor the health of their pets and obtain information that aids in early diagnosis. By providing a quick and accurate diagnostic tool for pet health management through deep learning, this study emphasizes the significance of developing an easily accessible service for home use.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.137-147
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• 2022

Diagnosis and management of skin condition is a very basic and important function in performing its role for workers in the beauty industry and cosmetics industry. For accurate skin condition diagnosis and management, it is necessary to understand the skin condition and needs of customers. In this paper, we developed SCIS, a big data-based skin care information system that supports skin condition diagnosis and management using social media big data for skin condition diagnosis and management. By using the developed system, it is possible to analyze and extract core information for skin condition diagnosis and management based on text information. The skin care information system SCIS developed in this paper consists of big data collection stage, text preprocessing stage, image preprocessing stage, and text word analysis stage. SCIS collected big data necessary for skin diagnosis and management, and extracted key words and topics from text information through simple frequency analysis, relative frequency analysis, co-occurrence analysis, and correlation analysis of key words. In addition, by analyzing the extracted key words and information and performing various visualization processes such as scatter plot, NetworkX, t-SNE, and clustering, it can be used efficiently in diagnosing and managing skin conditions.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.59-64
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• 2024

As the aging population grows, the incidence of cancer is increasing. Skin cancer appears externally, but people often don't notice it or simply overlook it. As a result, if the early detection period is missed, the survival rate in the case of late stage cancer is only 7.5-11%. However, the disadvantage of diagnosing, serious skin cancer is that it requires a lot of time and money, such as a detailed examination and cell tests, rather than simple visual diagnosis. To overcome these challenges, we propose an Attention-based CNN model skin cancer classification system. If skin cancer can be detected early, it can be treated quickly, and the proposed system can greatly help the work of a specialist. To mitigate the problem of image data imbalance according to skin cancer type, this skin cancer classification model applies the Over Sampling, technique to data with a high distribution ratio, and adds a pre-learning model without an Attention layer. This model is then compared to the model without the Attention layer. We also plan to solve the data imbalance problem by strengthening data augmentation techniques for specific classes.

• 한국벤처창업학회:학술대회논문집
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• 2017.04a
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• pp.45-45
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• 2017

본 연구에서는 고압산소치료 후 포유동물의 피부에서 Melanin 세포의 생성이 줄어들었음을 이미 보고된 바 있고, 치료 후 전층 피부이식 생존율을 높였고, 또한 인체의 피부 진피층 확인은 더 많은 임상데이터가 필요하나 고압산소치료 후 표피면의 변화는 빠른 시간에 확인 할 수 있다고 판단됨으로 이에 피부진단기의 기준면과의 평행(parallel polarization) 이미지로 피부표피의 변화를 확인 하고자 했다. 본 연구의 고압산소챔버를 활용한 피부표피에 대한 분석 및 실험 결과, 주름살 및 상처(Wrinkle & Scar) 분석에서 피실험자 남자1 3%, 남자2 2%, 여자1 5.9%, 여자2 2.3%로 피실험자 모두 감소 현상을 보여 피부 탄력도가 좋아 지는 효과를 보였다. 피부미백(S-Gray) 분석에서는 피실험자의 피부 표피면의 멜라닌 및 에리즈마 색소의 피부 톤이 남자1 1.1%, 남자2 2.3%, 여자1 4% 로 피실험자 4명 중 3명은 상승 효과를 얻었으나, 피실험자 다른 1명인 여자2는 2.3% 하향의 결과가 나타났다. 홍도(Erythema) 분석, 피실험자 정상인 부위인 D.BLUE/BLUE 값이 남자1 5.6%, 남자2 4.9%, 여자1 17.3%, 여자2 15.3% 증가 현상을 보였으며 남자와 여자의 효과 차이가 10% 이상으로 나타낸 것으로 보아 남자 보다는 여자가 우세한 것으로 판단되었으며, 비정상인 에리즈마 색소(민감도)의 비정상인 부위인 YELLOW/RED 컬러 값에서 남자1 5.2%, 남자2 5%, 여자1 9.2%, 여자2 4.5% 감소 하였음을 보였다. 이에 피실험자 모두 에리즈마인 민감성 산소치료에 따른 피부에 미치는 영향이 효과가 있는 것으로 판명되었다.

• Smart Media Journal
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• v.10 no.4
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• pp.45-54
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• 2021

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.