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Updated: Jun 19, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
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评估ResNet深度学习模型对两个频率参数对应的超声图像的否定性能
Hyekyoung Kang1, Chanrok Park2, Hyungjin Yang3
1Department of Radiological Science, College of Health & Medical, Shingu University, 377, Gwangmyeong-ro, Jungwon-gu, Seongnam-si 13174, Republic of Korea.
Bioengineering (Basel, Switzerland)
|July 27, 2024
概括
这项研究表明,深度学习的ResNet模型有效地减少了超声波图像中的高斯和斑点噪声,优于传统的过器. 这一进步提高了医学成像诊断的诊断准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 超声波成像是一种至关重要的非侵入性诊断工具.
- 图像质量通常会因高斯和斑点噪声而降低.
- 传统的空间域过技术用于降低噪音.
研究的目的:
- 评估基于卷积神经网络的残余网络 (ResNet),用于超声波图像降噪.
- 将ResNet模型的性能与传统的过方法进行比较.
主要方法:
- 使用了500张超声波图像的数据集,并添加了高斯和斑点噪声.
- 数据集被分为培训,验证和测试集 (8:1:1比).
- 一个16个剩余块的ResNet模型被训练了优化的超参数.
主要成果:
- 该ResNet深度学习模型展示了优越的降噪能力.
- 定量分析包括正常化的噪声功率频谱,峰值信号对噪声比率和根平均平方误差.
- ResNet的性能优于中位数,维纳和中位数修改的维纳过器.
结论:
- 深度学习,特别是ResNet,为超声波成像中的降噪提供了一种强大的方法.
- 这种技术可以提高图像质量,并可能提高诊断准确度.
- 在医疗图像处理方面,ResNet为传统的过方法提供了一个有希望的替代方案.
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