A.M. Al-Ansi , M. Almadi , V. Ryabtsev , T. Utkina
{"title":"基于磁共振成像结果数字化参数的脑肿瘤识别","authors":"A.M. Al-Ansi , M. Almadi , V. Ryabtsev , T. Utkina","doi":"10.1016/j.exco.2023.100125","DOIUrl":null,"url":null,"abstract":"<div><p>A methodology is proposed for identifying brain tumors by dividing the database into four parts. The results obtained from the study of sample specimens for each type of brain tumor showed a high degree of similarity in recognition. This methodology can be applied in healthcare facilities to improve the accuracy of disease diagnosis.</p></div>","PeriodicalId":100517,"journal":{"name":"Examples and Counterexamples","volume":"4 ","pages":"Article 100125"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666657X23000277/pdfft?md5=f3100a0e2172cdec05b25d019b3236c5&pid=1-s2.0-S2666657X23000277-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Identification of brain tumors based on digitized parameters from magnetic resonance imaging results\",\"authors\":\"A.M. Al-Ansi , M. Almadi , V. Ryabtsev , T. Utkina\",\"doi\":\"10.1016/j.exco.2023.100125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A methodology is proposed for identifying brain tumors by dividing the database into four parts. The results obtained from the study of sample specimens for each type of brain tumor showed a high degree of similarity in recognition. This methodology can be applied in healthcare facilities to improve the accuracy of disease diagnosis.</p></div>\",\"PeriodicalId\":100517,\"journal\":{\"name\":\"Examples and Counterexamples\",\"volume\":\"4 \",\"pages\":\"Article 100125\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666657X23000277/pdfft?md5=f3100a0e2172cdec05b25d019b3236c5&pid=1-s2.0-S2666657X23000277-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Examples and Counterexamples\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666657X23000277\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Examples and Counterexamples","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666657X23000277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of brain tumors based on digitized parameters from magnetic resonance imaging results
A methodology is proposed for identifying brain tumors by dividing the database into four parts. The results obtained from the study of sample specimens for each type of brain tumor showed a high degree of similarity in recognition. This methodology can be applied in healthcare facilities to improve the accuracy of disease diagnosis.