School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
Comput Biol Med. 2022 Sep;148:105812. doi: 10.1016/j.compbiomed.2022.105812. Epub 2022 Jul 8.
Breast cancer is a top dangerous killer for women. An accurate early diagnosis of breast cancer is the primary step for treatment. A novel breast cancer detection model called SAFNet is proposed based on ultrasound images and deep learning. We employ a pre-trained ResNet-18 embedded with the spatial attention mechanism as the backbone model. Three randomized network models are trained for prediction in the SAFNet, which are fused by majority voting to produce more accurate results. A public ultrasound image dataset is utilized to evaluate the generalization ability of our SAFNet using 5-fold cross-validation. The simulation experiments reveal that the SAFNet can produce higher classification results compared with four existing breast cancer classification methods. Therefore, our SAFNet is an accurate tool to detect breast cancer that can be applied in clinical diagnosis.
乳腺癌是女性的头号危险杀手。乳腺癌的准确早期诊断是治疗的首要步骤。本文提出了一种基于超声图像和深度学习的新型乳腺癌检测模型，称为 SAFNet。我们使用预训练的 ResNet-18 嵌入空间注意力机制作为骨干模型。在 SAFNet 中训练了三个随机网络模型进行预测，通过多数投票融合产生更准确的结果。利用公共超声图像数据集，通过 5 折交叉验证评估我们的 SAFNet 的泛化能力。仿真实验表明，与四种现有的乳腺癌分类方法相比，SAFNet 可以产生更高的分类结果。因此，我们的 SAFNet 是一种用于临床诊断的准确的乳腺癌检测工具。
