HED-Net:一种混合集体深度学习框架,用于乳房超声波图像分类
Soumya Sara Koshy1, L Jani Anbarasi1, Modigari Narendra1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Frontiers in artificial intelligence
|February 9, 2026
概括
这项研究介绍了HED-Net,这是用于乳房超声波图像分类的混合深度学习模型. 该框架显著提高了诊断准确性,减少了解释时间,对临床应用有希望.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 乳腺癌的诊断严重依赖于医学成像,超声波是关键的方法.
- 准确有效地解释乳房超声波图像对于及时诊断和治疗至关重要.
- 深度学习为自动化和提高图像分析的准确性提供了潜力.
研究的目的:
- 开发和评估用于乳房超声波图像分类的混合深度学习整体框架.
- 用人工智能提高乳腺癌诊断的准确性和效率.
- 提高医学成像中的深度学习模型的可解释性和稳定性.
主要方法:
- 开发了一个混合深度学习整体框架,HED-Net,结合了EfficientNetB7,DenseNet121和ConvNeXtTiny卷积神经网络模型.
- 单个模型被并行训练,以提取不同的特征 (本地,结构,全球).
- 使用XGBoost实现了特征融合,用于概率平均的软投票组合,以及用于解释性的SHAP/Grad-CAM.
主要成果:
- 在多个数据集 (BUSI,BUS-UCLM,UDIAT) 中,HED-Net框架实现了高性能.
- 例如,在UDIAT数据集上,准确度达到了96.97%,精度达到了100.00%,回忆达到了90.91%,F1得分达到了95.24%,AUC达到了99.17%.
- 通过SHAP和Grad-CAM可视化,模型的解释性得到了增强.
结论:
- 拟议的HED-Net框架显示了乳房超声波图像分类中临床应用的巨大潜力.
- 它提供了更高的诊断准确性和更短的解释时间,在资源有限的环境中尤其有价值.
- 通过先进的可视化技术来提高模型的稳定性和透明度.
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