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Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning.

Authors :
Guo, Ying-Ying
Huang, Yin-Hui
Wang, Yi
Huang, Jing
Lai, Qing-Quan
Li, Yuan-Zhe
Source :
Computational & Mathematical Methods in Medicine. 8/31/2022, p1-9. 9p.
Publication Year :
2022

Abstract

Background. Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today's clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional neural network are output using a support vector machine in this technique. During the testing phase, the convolutional neural network's labeled output, as well as the test grayscale picture, is passed to the SVM classifier for accurate segmentation. Results. We tested on the collected breast tumor dataset and found that our proposed combined CNN-SVM network achieved 0.93, 0.95, and 0.92 on DSC coefficient, PPV, and sensitivity index, respectively. We also compare with the segmentation frameworks of other papers, and the comparison results prove that our CNN-SVM network performs better and can accurately segment breast tumors. Conclusion. Our proposed CNN-SVM combined network achieves good segmentation results on the breast tumor dataset. The method can adapt to the differences in breast tumors and segment breast tumors accurately and efficiently. It is of great significance for identifying triple-negative breast cancer in the future. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1748670X
Database :
Academic Search Index
Journal :
Computational & Mathematical Methods in Medicine
Publication Type :
Academic Journal
Accession number :
158815523
Full Text :
https://doi.org/10.1155/2022/2541358