Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning
Conclusion: Machine learning, particularly the multifeature fusion SVM model, shows significant potential in diagnosing new nodules after breast cancer surgery. It can assist doctors in developing more effective treatment plans, improving patient outcomes. Future studies should expand sample sizes,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | The Breast Journal |
| Online Access: | http://dx.doi.org/10.1155/tbj/8511049 |
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| _version_ | 1850189294736506880 |
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| author | Zhixiang Wang Qingqing Li Yiran Wang Linxue Qian Xiangdong Hu Dong Liu |
| author_facet | Zhixiang Wang Qingqing Li Yiran Wang Linxue Qian Xiangdong Hu Dong Liu |
| author_sort | Zhixiang Wang |
| collection | DOAJ |
| description | Conclusion: Machine learning, particularly the multifeature fusion SVM model, shows significant potential in diagnosing new nodules after breast cancer surgery. It can assist doctors in developing more effective treatment plans, improving patient outcomes. Future studies should expand sample sizes, include multicenter data, and explore advanced algorithms to further enhance diagnostic performance. |
| format | Article |
| id | doaj-art-16b9c1b74fa343aba6985e182fa7e8a2 |
| institution | OA Journals |
| issn | 1524-4741 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Breast Journal |
| spelling | doaj-art-16b9c1b74fa343aba6985e182fa7e8a22025-08-20T02:15:38ZengWileyThe Breast Journal1524-47412025-01-01202510.1155/tbj/8511049Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine LearningZhixiang Wang0Qingqing Li1Yiran Wang2Linxue Qian3Xiangdong Hu4Dong Liu5Department of UltrasoundDepartment of UltrasoundHonors CollegeDepartment of UltrasoundDepartment of UltrasoundDepartment of UltrasoundConclusion: Machine learning, particularly the multifeature fusion SVM model, shows significant potential in diagnosing new nodules after breast cancer surgery. It can assist doctors in developing more effective treatment plans, improving patient outcomes. Future studies should expand sample sizes, include multicenter data, and explore advanced algorithms to further enhance diagnostic performance.http://dx.doi.org/10.1155/tbj/8511049 |
| spellingShingle | Zhixiang Wang Qingqing Li Yiran Wang Linxue Qian Xiangdong Hu Dong Liu Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning The Breast Journal |
| title | Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning |
| title_full | Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning |
| title_fullStr | Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning |
| title_full_unstemmed | Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning |
| title_short | Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning |
| title_sort | diagnosis of benign and malignant newly developed nodules on the surgical side after breast cancer surgery based on machine learning |
| url | http://dx.doi.org/10.1155/tbj/8511049 |
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