• 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 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 Korea Multimedia Society
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• v.21 no.2
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• pp.130-137
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• 2018

Recently, human life is getting longer due to change of living environment and development of medical technology, and silver medical technology has been in the limelight. Geriatric skin disease is difficult to detect early, and when it is missed, it becomes a malignant disease and is difficult to treatment. Melanoma is one of the most common diseases of geriatric skin disease and initially has a similar modality with the nevus. In order to overcome this problem, we attempted to perform a feature analysis in order to attempt automatic detection of melanoma-like lesions. In this paper, one is first order analysis using information of pixels in radiomic feature. The other is a gray-level co-occurrence matrix and a gray level run length matrix, which are feature extraction methods for converting image information into a matrix. The features were extracted through these analyses. And classification is implemented by SVM.

• Journal of Clinical Otolaryngology Head and Neck Surgery
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• v.29 no.2
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• pp.259-263
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• 2018

There are many treatment options for the malignant melanoma. Wide excisional surgery is one of the most acceptable treatments for locoregional treatment. Depending on the pathologic classification, however, some other treatment option can be included such as chemotherapy, radiotherapy and immunotherapy as adjuvant treatment. Small cell type malignant melanoma is a rare variant of malignant melanoma. It is known that melanomas manifesting this morphology are invariably in vertical growth phase and have an aggressive course. The authors encountered small cell type malignant melanoma and would like to share the experience of successful treatment with surgery plus immunotherapy as one of adjuvant treatment options.

• Journal of Korean Foot and Ankle Society
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• v.10 no.1
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• pp.18-23
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• 2006

Purpose: We reviewed the clinical finding of malignant melanoma of the foot in korean because it's advanced stage and extended lesion at diagnosis. Materials and Methods: Retrospective study was enforced about the 11 cases who has diagnosed to malignant melanoma of the foot from February 1995 to March 2004. The mean follow up period was 61 months. In this study we used age, sex, site, depth, histology, clinical stage, precursor lesion, misdiagnosis, interval to diagnosis, survival time, survival. Results: Average age was 58 years and number of female was six. Common site of involvement were heel of plantar surface (6 cases) and subungual area (2 cases). Depths of involvement were 0.3 to 10 mm, most common histological type was acral lentiginous melanoma (7 cases), stage 5 according to classification of Clark were 5 cases and stage 2 or more according to clinical staging were 8 cases. precursor lesion were benign melanocytic nevi (2 cases) and ill defined (9 cases). Chief complaint were increasing of size, color change, pain and ulceration. Conclusion: Malignant melanoma of the foot usually arise at nonvisible area and is easy to be misdiagnosed or delayed treatment. So it is hard to early diagnosis and have poor prognosis. So we need education and effort to early detection and diagnosis.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.34 no.6
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• pp.611-615
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• 2008

The prognosis of oral malignant melanoma is poor compared with cutaneous melanoma. It may be related to the difficulty of wide enough resection, the early hematogenous matastases, higher stage at initial diagnosis, and tendency to growth vertically. In the view of histological differences between oral mucosa and skin, it is impossible use Clark's and Breslow's classifications for prognosis. The great problem is that there is still no consensus on the treatment due to rarity. Because data collection from case reports is considered to be the best source of information and should be pooled to analyze key determinants of outcome, We analysed 6 cases of primary malignant melanoma of the oral cavity which were diagnosed and treated in Pusan National University Hospital on recent 7 years and reviewed the literatures. Immunohistochemical study on S 100 Protein, GP 100 (HMB-45) with biopsy was usable to confirm the melanoma. Three patients who were treated by surgery, chemotherapy are alive, but a patients who couldn't received benefit care surgically due to poor condition was died of distant metastasis, and two patients who refused to surgery are still alive. Neck dissection including wide excision is recommended if lymph node involvement is suspected. Additionally, adjuvant chemotherapy could be considered as supporting therapy for malignant melanoma.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.3219-3221
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• 2013

Background: This study used receiver operating characteristic (ROC) curves to screen Surveillance, Epidemiology and End Results (SEER) skin melanoma data to identify and quantify the effects of socioeconomic factors on cause specific survival. Methods: 'SEER cause-specific death classification' used as the outcome variable. The area under the ROC curve was to select best pretreatment predictors for further multivariate analysis with socioeconomic factors. Race and other socioeconomic factors including rural-urban residence, county level % college graduate and county level family income were used as predictors. Univariate and multivariate analyses were performed to identify and quantify the independent socioeconomic predictors. Results: This study included 49,999 parients. The mean follow up time (SD) was 59.4 (17.1) months. SEER staging (ROC area of 0.08) was the most predictive foctor. Race, lower county family income, rural residence, and lower county education attainment were significant univariates, but rural residence was not significant under multivariate analysis. Living in poor neighborhoods was associated with a 2-4% disadvantage in actuarial cause specific survival. Conclusions: Racial and socioeconomic factors have a significant impact on the survival of melanoma patients. This generates the hypothesis that ensuring access to cancer care may eliminate these outcome disparities.

• Biomolecules & Therapeutics
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• v.27 no.3
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• pp.302-310
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• 2019

Melanoma cells have been shown to respond to BRAF inhibitors; however, intrinsic and acquired resistance limits their clinical application. In this study, we performed RNA-Seq analysis with BRAF inhibitor-sensitive (A375P) and -resistant (A375P/Mdr with acquired resistance and SK-MEL-2 with intrinsic resistance) melanoma cell lines, to reveal the genes and pathways potentially involved in intrinsic and acquired resistance to BRAF inhibitors. A total of 546 differentially expressed genes (DEGs), including 239 up-regulated and 307 down-regulated genes, were identified in both intrinsic and acquired resistant cells. Gene ontology (GO) analysis revealed that the top 10 biological processes associated with these genes included angiogenesis, immune response, cell adhesion, antigen processing and presentation, extracellular matrix organization, osteoblast differentiation, collagen catabolic process, viral entry into host cell, cell migration, and positive regulation of protein kinase B signaling. In addition, using the PAN-THER GO classification system, we showed that the highest enriched GOs targeted by the 546 DEGs were responses to cellular processes (ontology: biological process), binding (ontology: molecular function), and cell subcellular localization (ontology: cellular component). Ingenuity pathway analysis (IPA) network analysis showed a network that was common to two BRAF inhibitorresistant cells. Taken together, the present study may provide a useful platform to further reveal biological processes associated with BRAF inhibitor resistance, and present areas for therapeutic tool development to overcome BRAF inhibitor resistance.

• Korean Journal of Materials Research
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• v.33 no.3
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• pp.87-94
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• 2023

Exposure to ultraviolet (UV) light is often associated with skin damage, sometimes very serious, and in recent times has received particular attention as a health risk. As a result, the proper use of sunscreen has long been recommended to protect against skin damage. The continued increase in the use of sunscreen may be linked to increased information about the risk of melanoma and non-melanoma skin cancer caused by prolonged exposure to ultraviolet rays. Natural and harmless materials that block and prevent UV light have emerged as essential household items in the field of skin beauty. New materials need to be considered and evaluated in relation to ultraviolet rays and their harmful effects. This study aims to explain the effect of UV exposure on human skin, the classification of sunscreens, the application of zeolite, nano clay, and LDH in sunscreen formulations, as well as the regulation of this service in various countries around the world.

• 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.