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.

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.

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