CLGB-Net: fusion network for identifying local and global information of lesions in digital mammography images

Abstract

Worldwide, breast cancer ranks among the cancers with the highest incidence rate. Early diagnosis is crucial to improve the survival rate of patients. Digital Mammography (DM) is widely used for breast cancer diagnosis. The disadvantage is that DM relies too much on the doctor's experience, which can easily lead to missed diagnosis and misdiagnosis. In order to address the shortcomings of traditional methods, a CLGB-Net deep learning model integrating local and global information is proposed for the early screening of breast cancer. Four network architectures are integrated into the CLGB-Net model: ResNet-50, Swin Transformer, Feature Pyramid Network (FPN), and Class Activation Mapping (CAM). ResNet-50 is used to extract local features. The Swin Transformer is utilized to capture global contextual information and extract global features. FPN achieves efficient fusion of multi-scale features. CAM generates a class activation weight matrix to weight the feature map, thereby enhancing the sensitivity and classification performance of the model to key regions. In breast cancer early screening, the CLGB-Net demonstrates the following performance metrics: a precision of 0.900, recall of 0.935, F1-score of 0.900, and final accuracy of 0.904. Experimental data from 3,552 samples, including normal, benign, and malignant cases, support these results. The precision of this model was improved by 0.182, 0.038, 0.023, and 0.021 compared to ResNet-50, ResNet-101, Vit Transformer, and Swin Transformer, respectively. The CLGB-Net model is capable of capturing both local and global information, particularly in terms of sensitivity to subtle details. It significantly improves the accuracy and robustness of identifying lesions in mammography images and reduces the risk of missed diagnosis and misdiagnosis.

Keywords: CAD; CLGB-Net; breast cancer; deep learning; early screening.

Figure 1

ResNet-50 structure diagram.

Figure 2

Swin Transformer structure diagram.

Figure 3

FPN structure diagram.

Figure 4

CAM structure diagram.

Figure 5

CLGB-Net technology roadmap.

Figure 6

Confusion Matrix.

Figure 7

Accuracy of CLGB-Net model.

Figure 8

Evaluation indicators for different detection algorithms.

Figure 9

Multi-class ROC curves for CLGB-Net.

Figure 10

Evaluation of recognition accuracy by different modules of CLGB-Net.