{"title":"数字乳房x光图像中计算机辅助质量检测的模糊推理系统设计","authors":"Volkan Goreke, E. Uzunhisarcikli, B. Oztoprak","doi":"10.1109/TIPTEKNO.2015.7374537","DOIUrl":null,"url":null,"abstract":"Breast cancer is the most common cancer in women. A mammogram is an X-ray of the breast, using very low levels of radiation. Artificial intelligence and fuzzy inference techniques can be used in CAD systems. These systems generally have main phases that the their names are image processing, and classification. In this study, we used images of mammogram that were obtained MIAS database. The fuzzy inference system was designed using image processing tecniques and statical features. The system was tested and for sensitivity and Specificity respectively, %98 and %99 was found. This study gave better results than our earlier studies using artificial neural network that have %96 sensivity and %96 specifity.","PeriodicalId":143218,"journal":{"name":"2015 19th National Biomedical Engineering Meeting (BIYOMUT)","volume":"441 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A fuzzy inference system design for computer aided mass detection in digital mammogram images\",\"authors\":\"Volkan Goreke, E. Uzunhisarcikli, B. Oztoprak\",\"doi\":\"10.1109/TIPTEKNO.2015.7374537\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Breast cancer is the most common cancer in women. A mammogram is an X-ray of the breast, using very low levels of radiation. Artificial intelligence and fuzzy inference techniques can be used in CAD systems. These systems generally have main phases that the their names are image processing, and classification. In this study, we used images of mammogram that were obtained MIAS database. The fuzzy inference system was designed using image processing tecniques and statical features. The system was tested and for sensitivity and Specificity respectively, %98 and %99 was found. This study gave better results than our earlier studies using artificial neural network that have %96 sensivity and %96 specifity.\",\"PeriodicalId\":143218,\"journal\":{\"name\":\"2015 19th National Biomedical Engineering Meeting (BIYOMUT)\",\"volume\":\"441 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 19th National Biomedical Engineering Meeting (BIYOMUT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TIPTEKNO.2015.7374537\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 19th National Biomedical Engineering Meeting (BIYOMUT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TIPTEKNO.2015.7374537","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A fuzzy inference system design for computer aided mass detection in digital mammogram images
Breast cancer is the most common cancer in women. A mammogram is an X-ray of the breast, using very low levels of radiation. Artificial intelligence and fuzzy inference techniques can be used in CAD systems. These systems generally have main phases that the their names are image processing, and classification. In this study, we used images of mammogram that were obtained MIAS database. The fuzzy inference system was designed using image processing tecniques and statical features. The system was tested and for sensitivity and Specificity respectively, %98 and %99 was found. This study gave better results than our earlier studies using artificial neural network that have %96 sensivity and %96 specifity.