{"title":"使用改进的惯性投影算法进行乳腺癌筛查的分割可行性问题。","authors":"Pennipat Nabheerong, Warissara Kiththiworaphongkich, Watcharaporn Cholamjiak","doi":"10.1155/2023/2060375","DOIUrl":null,"url":null,"abstract":"<p><p>To detect breast cancer in mammography screening practice, we modify the inertial relaxed CQ algorithm with Mann's iteration for solving split feasibility problems in real Hilbert spaces to apply in an extreme learning machine as an optimizer. Weak convergence of the proposed algorithm is proved under certain mild conditions. Moreover, we present the advantage of our algorithm by comparing it with existing machine learning methods. The highest performance value of 85.03% accuracy, 82.56% precision, 87.65% recall, and 85.03% F1-score show that our algorithm performs better than the other machine learning models.</p>","PeriodicalId":46159,"journal":{"name":"International Journal of Breast Cancer","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501843/pdf/","citationCount":"0","resultStr":"{\"title\":\"Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems.\",\"authors\":\"Pennipat Nabheerong, Warissara Kiththiworaphongkich, Watcharaporn Cholamjiak\",\"doi\":\"10.1155/2023/2060375\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>To detect breast cancer in mammography screening practice, we modify the inertial relaxed CQ algorithm with Mann's iteration for solving split feasibility problems in real Hilbert spaces to apply in an extreme learning machine as an optimizer. Weak convergence of the proposed algorithm is proved under certain mild conditions. Moreover, we present the advantage of our algorithm by comparing it with existing machine learning methods. The highest performance value of 85.03% accuracy, 82.56% precision, 87.65% recall, and 85.03% F1-score show that our algorithm performs better than the other machine learning models.</p>\",\"PeriodicalId\":46159,\"journal\":{\"name\":\"International Journal of Breast Cancer\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501843/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Breast Cancer\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/2060375\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Breast Cancer","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2023/2060375","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems.
To detect breast cancer in mammography screening practice, we modify the inertial relaxed CQ algorithm with Mann's iteration for solving split feasibility problems in real Hilbert spaces to apply in an extreme learning machine as an optimizer. Weak convergence of the proposed algorithm is proved under certain mild conditions. Moreover, we present the advantage of our algorithm by comparing it with existing machine learning methods. The highest performance value of 85.03% accuracy, 82.56% precision, 87.65% recall, and 85.03% F1-score show that our algorithm performs better than the other machine learning models.
期刊介绍:
International Journal of Breast Cancer is a peer-reviewed, Open Access journal that provides a forum for scientists, clinicians, and health care professionals working in breast cancer research and management. The journal publishes original research articles, review articles, and clinical studies related to molecular pathology, genomics, genetic predisposition, screening and diagnosis, disease markers, drug sensitivity and resistance, as well as novel therapies, with a specific focus on molecular targeted agents and immune therapies.