Kurnia Zikir Bimastro, T. Purboyo, C. Setianingsih, M. A. Murti
{"title":"基于Android的太阳Lentigo恶性黑色素瘤图像的潜在检测","authors":"Kurnia Zikir Bimastro, T. Purboyo, C. Setianingsih, M. A. Murti","doi":"10.1109/ICITISEE48480.2019.9003743","DOIUrl":null,"url":null,"abstract":"Solar Lentigines is a skin disease caused by frequent exposure to direct sunlight. Appearance in solar lentigines can resemble Lentigo Malignant Melanoma cancer at an early stage. solar lentigines a disease that is not dangerous and does not require special treatment, but if there are significant changes such as asymmetrical wounds, obscure borders, non-homogeneous colors, diameters exceeding 6 millimeters, solar lentigines are suspected as lentigo malignant early stage melanoma. lentigo malignant melanoma is a rare but dangerous type of skin cancer if it is not treated immediately with asymmetrical, unclear boundaries, non-homogeneous colors, diameters exceeding 6 millimeters. This research aims to help detect the potential of lentigo malignant melanoma disease by using the image of solar lentigines. This application uses the ABCD method for feature extraction to scratch the input image and decision tree for classification. ABCD method is a medical method used to detect cancer in terms of asymmetry, obscure borders, color, diameter. The data of this research were obtained from one hospital in Bandung and the data was presented in table form and explained informally. The result of the application is a diagnosis of the potential for disease. The accuracy value of this application is 97.5% from 60 training data.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Potential Detection of Lentigo Maligna Melanoma on Solar Lentigines Image Based on Android\",\"authors\":\"Kurnia Zikir Bimastro, T. Purboyo, C. Setianingsih, M. A. Murti\",\"doi\":\"10.1109/ICITISEE48480.2019.9003743\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Solar Lentigines is a skin disease caused by frequent exposure to direct sunlight. Appearance in solar lentigines can resemble Lentigo Malignant Melanoma cancer at an early stage. solar lentigines a disease that is not dangerous and does not require special treatment, but if there are significant changes such as asymmetrical wounds, obscure borders, non-homogeneous colors, diameters exceeding 6 millimeters, solar lentigines are suspected as lentigo malignant early stage melanoma. lentigo malignant melanoma is a rare but dangerous type of skin cancer if it is not treated immediately with asymmetrical, unclear boundaries, non-homogeneous colors, diameters exceeding 6 millimeters. This research aims to help detect the potential of lentigo malignant melanoma disease by using the image of solar lentigines. This application uses the ABCD method for feature extraction to scratch the input image and decision tree for classification. ABCD method is a medical method used to detect cancer in terms of asymmetry, obscure borders, color, diameter. The data of this research were obtained from one hospital in Bandung and the data was presented in table form and explained informally. The result of the application is a diagnosis of the potential for disease. The accuracy value of this application is 97.5% from 60 training data.\",\"PeriodicalId\":380472,\"journal\":{\"name\":\"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITISEE48480.2019.9003743\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITISEE48480.2019.9003743","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Potential Detection of Lentigo Maligna Melanoma on Solar Lentigines Image Based on Android
Solar Lentigines is a skin disease caused by frequent exposure to direct sunlight. Appearance in solar lentigines can resemble Lentigo Malignant Melanoma cancer at an early stage. solar lentigines a disease that is not dangerous and does not require special treatment, but if there are significant changes such as asymmetrical wounds, obscure borders, non-homogeneous colors, diameters exceeding 6 millimeters, solar lentigines are suspected as lentigo malignant early stage melanoma. lentigo malignant melanoma is a rare but dangerous type of skin cancer if it is not treated immediately with asymmetrical, unclear boundaries, non-homogeneous colors, diameters exceeding 6 millimeters. This research aims to help detect the potential of lentigo malignant melanoma disease by using the image of solar lentigines. This application uses the ABCD method for feature extraction to scratch the input image and decision tree for classification. ABCD method is a medical method used to detect cancer in terms of asymmetry, obscure borders, color, diameter. The data of this research were obtained from one hospital in Bandung and the data was presented in table form and explained informally. The result of the application is a diagnosis of the potential for disease. The accuracy value of this application is 97.5% from 60 training data.