• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.256-263
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• 2022

Skin cancer is one of the most common diseases in the world, and the incidence rate in Korea has increased by about 100% over the past five years. In the United States, more than 5 million people are diagnosed with skin cancer every year. Skin cancer mainly occurs when skin tissue is damaged for a long time due to exposure to ultraviolet rays. Melanoma, a malignant tumor of skin cancer, is similar in appearance to Atypical melanocytic nevus occurring on the skin, making it difficult for the general public to be aware of it unless secondary signs occur. In this paper, we propose a skin cancer lesion edge detection algorithm and a deep learning model, CRNN, which performs skin cancer lesion classification for early detection and classification of these skin cancers. As a result of the experiment, when using the contour detection algorithm proposed in this paper, the classification accuracy was the highest at 97%. For the Canny algorithm, 78% was shown, 55% for Sobel, and 46% for Laplacian.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.555-560
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• 2021

Recently, as people's interest in facial skin beauty has increased, research on skin disease recognition for facial skin beauty is being conducted by using deep learning. These studies recognized a variety of skin diseases, including acne. Existing studies can recognize only the single skin diseases, but skin diseases that occur on the face can enact in a more diverse and complex manner. Therefore, in this paper, complex skin diseases such as acne, blackheads, freckles, age spots, normal skin, and whiteheads are identified using the Inception-ResNet V2 deep learning mode with multi-label classification. The accuracy was 98.8%, hamming loss was 0.003, and precision, recall, F1-Score achieved 96.6% or more for each single class.

• Proceedings of the KAIS Fall Conference
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• 2010.11b
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• pp.878-881
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• 2010

For better diagnosis and prescription in Korean traditional medicine, Lee Je-Ma (1837-1900) created Sasang Constitution classification which was divided into four groups of Taeyangin, Soyangin, Taeumin and Soumin based on both body shape and natural disposition. The purpose of this study was to investigate the characteristics of facial skin parameters (hydration, lipid and pH) on forehead and cheek according to Sasang Constitution classifications of Taeumin, Soyangin and Soumin in Korean. Eighty-nine Korean female subjects were recruited for this study and the average age of them was 19.9${\pm}$0.84 years. The four groups by the Sasang Constitution were classified by questionnaire for the Sasang Constitution classification proposed by Kyung-Hee Oriental Medicine Hospital. Consequently, thirty-eight (42.7%) among the subjects were grouped into Soumin, twenty-nine (32.6%) into Taeumin, twenty (22.5%) into Soyangin and two (2%) into Taeyangin. Taeyangin group was excluded from statistical analysis due to small subjects. Hydration, lipid and pH parameters on forehead and cheek were measured by using non-invasive instruments of Corneometer (CM 825, Schwarzhaup, Germany), Sebumeter (SM 815, Schwarzhaup, Germany) and Skin-pH-meter (pH 905, Schwarzhaup, Germany), respectively. The measurements by the same investigator were performed under standardized condition with a room temperature of $21^{\circ}C$ and a humidity level of 40% to 50%. As a result, hydration (F=25.481, p=.000), lipid (F=5.753, p=.005) and pH (F=5.010, p=.009) of the forehead skin showed significant differences in the order of Taeumin, Soyangin and Soumin. Hydration (F=23.216, p=.000), lipid (F=6.898 p=.002) and pH (F=5.070, p=.008) of the cheek skin showed significant differences in the order of Taeumin, Soyangin and Soumin. In conclusion, facial skin surface seemed to be dependent on Sasang Constitution classification in Korean. These findings indicated that Sasang Constitution classification might be an useful esthetic treatment for caring facial skin in the future.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.69-77
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• 2018

The malignant melanoma accounts for about 1 to 3% of the total malignant tumor in the West, especially in the US, it is a disease that causes more than 9,000 deaths each year. Generally, skin lesions are difficult to detect the features through photography. In this paper, we propose a computer-aided diagnosis algorithm based on deep learning for classification of malignant melanoma and benign skin tumor in RGB channel skin images. The proposed deep learning model configures the tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to segment a skin lesion area in the dermoscopic image. We could implement algorithms to classify malignant melanoma and benign tumor using skin lesion image and results of expert's labeling in ResNet. The U-Net model obtained a dice similarity coefficient of 83.45% compared with results of expert's labeling. The classification accuracy of malignant melanoma obtained the 83.06%. As the result, it is expected that the proposed artificial intelligence algorithm will utilize as a computer-aided diagnosis algorithm and help to detect malignant melanoma at an early stage.

• 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.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1203-1211
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• 2022

Melanoma is a skin cancer that starts in pigment-producing cells (melanocytes). The death rates of skin cancer like melanoma can be reduced by early detection and diagnosis of diseases. It is common for doctors to spend a lot of time trying to distinguish between skin lesions and healthy cells because of their striking similarities. The detection of melanoma lesions can be made easier for doctors with the help of an automated classification system that uses deep learning. This study presents a new approach for melanoma classification based on an ensemble of deep convolution neural networks and a Log-Gabor filter. First, we create the Log-Gabor representation of the original image. Then, we input the Log-Gabor representation into a new ensemble of deep convolution neural networks. We evaluated the proposed method on the melanoma dataset collected at Yonsei University and Dongsan Clinic. Based on our numerical results, the proposed framework achieves more accuracy than other approaches.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.127-132
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• 2011

In this paper, we propose a novel method of adult image classification by combining skin color regions and edges in an input image. The proposed method consists of four steps. In the first step, initial skin color regions are detected by logical AND operation of all skin color regions detected by the existing methods of skin color detection. In the second step, a skin color probability map is created by modeling the distribution of skin color in the initial regions. Then, a binary image is generated by using threshold value from the skin color probability map. In the third step, after using the binary image and edge information, we detect final skin color regions using a region growing method. In the final step, adult image classification is performed by support vector machine(SVM). To this end, a feature vector is extracted by combining the final skin color regions and neighboring edges of them. As experimental results, the proposed method improves performance of the adult image classification by 9.6%, compared to the existing method.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.162.4-162
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• 2001

This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3375-3379
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• 2010

Objective: The purpose of this study was to investigate the characteristics of facial skin parameters (hydration, lipid and pH) on forehead and cheek according to Ayurvedic constitution classification of Vata, Pitta and Kapha. The condition of hydration, lipid and pH in the facial skin was measured using non-invasive diagnostic technique. The collected data was analyzed with the SPSS 16.0 windows statistical program. Design: Eighty-nine Korean female subjects were recruited for this study and the average age of them was $19.9{\pm}0.84$ years. Three groups by the Ayurvedic constitution were classified by questionnaire. Results: There was a significant difference in hydration, lipid and pH according to Ayurvedic constitution. The measurement of hydration on the face depending on the constitution were shown in the order of Pitta, Kapha and Vata (p<0.001). The measurement of lipid on the face depending on the constitution were shown in the order of Kapha, Pitta and Vata (p<0.001, p<0.01). The measurement of pH on the face depending on the constitution were shown in the order of Kapha, Pitta and Vata (p<0.01). Conclusion: Facial skin surface seemed to be dependent on Ayurvedic constitution classification in Korean. These findings indicated that Ayurvedic constitution classification might be a useful esthetic treatment for caring facial skin in the future.