Research on breast cancer image segmentation method based on improved ResNet34-UNet hybrid loss function
TL;DR
The proposed improved U-Net-based deep learning model for image segmentation provides an effective solution for automated, high-precision segmentation of breast ultrasound images, demonstrating considerable potential for clinical translation.
Abstract
Breast cancer ultrasound images present challenges such as heterogeneous lesion morphologies, indistinct boundaries, and class imbalance. To overcome these difficulties, we propose an improved U-Net-based deep learning model for image segmentation. Specifically, we employ ResNet-34 as the encoder to form a ResNet34-UNet, which enhances multi-scale feature extraction and facilitates gradient flow through residual connections. In this architecture, the initial 7×7 convolution is replaced by three cascaded 3×3 convolutions. This preserves the original receptive field size but achieves gains in parameter efficiency and nonlinear modeling capacity. Furthermore, a hybrid loss function combining Weighted Binary Cross-Entropy and Dice loss is introduced to improve the detection of small lesions and the precision of boundary segmentation. The proposed method is evaluated on two public breast ultrasound datasets. Experimental results show that it significantly outperforms the standard U-Net and the original ResNet baseline in terms of accuracy, precision, recall, and Intersection over Union (IoU). The proposed approach provides an effective solution for automated, high-precision segmentation of breast ultrasound images, demonstrating considerable potential for clinical translation.