{"title":"基于SVM算法的乳腺超声肿瘤分类","authors":"P. Acevedo, M. Vazquez","doi":"10.1109/CSCI49370.2019.00128","DOIUrl":null,"url":null,"abstract":"In this work tumor classification was performed using K-means and GLCM algorithms to segment ultrasound images. In order to apply Stavros criteria, a lineal support vector machine (SVM) algorithm was used to classify benign and malignant tumors. 94% of echographies were correctly classified.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Classification of Tumors in Breast Echography Using a SVM Algorithm\",\"authors\":\"P. Acevedo, M. Vazquez\",\"doi\":\"10.1109/CSCI49370.2019.00128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work tumor classification was performed using K-means and GLCM algorithms to segment ultrasound images. In order to apply Stavros criteria, a lineal support vector machine (SVM) algorithm was used to classify benign and malignant tumors. 94% of echographies were correctly classified.\",\"PeriodicalId\":103662,\"journal\":{\"name\":\"2019 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSCI49370.2019.00128\",\"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 International Conference on Computational Science and Computational Intelligence (CSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCI49370.2019.00128","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of Tumors in Breast Echography Using a SVM Algorithm
In this work tumor classification was performed using K-means and GLCM algorithms to segment ultrasound images. In order to apply Stavros criteria, a lineal support vector machine (SVM) algorithm was used to classify benign and malignant tumors. 94% of echographies were correctly classified.
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