Breast cancer molecular typing prediction based on transfer learning and support vector machine(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第5期
- Page:
- 635-639
- Research Field:
- 其他（激光医学等）
- Publishing date:
Info
- Title:
- Breast cancer molecular typing prediction based on transfer learning and support vector machine
- Author(s):
- ZHAO Qingyi; LIN Yong
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
- Keywords:
- Keywords: breast cancer PET/CT transfer learning Xception support vector machine
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.05.019
- Abstract:
- Abstract: Molecular typing of breast cancer plays a decisive reference role in the treatment of breast cancer. Traditional typing methods are invasive and may have the problem of false positive, and the accuracy of existing imaging-based typing methods is unsatisfactory. Therefore, a feature extraction method using transfer learning, combined with typing prediction method of support vector machine (SVM), is proposed in the study. After the fusion and normalization of breast cancer PET/CT marker images, Xception transfer learning network is used for feature extraction, and finally SVM is used for classification and typing. The performance evaluation on test set shows that the accuracy of Xception+SVM model is 0.687, and the AUC is 0.787, better than the existing imaging-based methods, which verifies the effectiveness of the proposed method.
Last Update: 2022-05-27