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

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.

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning

“…The attention module used in this paper makes the segmentation model pay more attention to the local feature information of the tumor nucleus and the enhanced tumor region, ignoring the global feature information of the whole tumor, resulting in a slight decrease in the segmentation effect of the whole tumor. In the future segmentation model research, the local feature- 9 Computational and Mathematical Methods in Medicine level global feature information of will be fused at the same time, in order to improve the segmentation accuracy of the whole tumor [40]. Note that volumetric modelling and visualization [41,42] pertaining to the breast tumor structures can help in the analysis of breast cancer for future implementation.…”

A Survey of Convolutional Neural Network in Breast Cancer

“…Guo et al [ 201 ] proposed a novel network to segment breast cancer. They designed a 6-layers CNN model, which consisted of two convolutional layers, two pooling layers, and two fully connected layers.…”

Construction of a risk prediction model based on the analysis of MRI imaging features for patients with triple-negative breast cancer and validation of its efficacy

“…The incidence of this disease has increased over recent years with an increasing trend to affect younger patients. Consequently, the overall size of the patient population is increasing annually, thus generating a serious threat to the health and lives of women [1,2]. Triple-negative breast cancer is a special type of breast cancer, which refers to the simultaneous negative expression of the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor.…”