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基于自适应多神经网络的胎盘超声图像自动分级研究

DOI: 10.11834/jig.200607166

Keywords: 胎盘超声图像,自动分级,自适应多神经网络,阈值分割,两级分离

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Abstract:

针对胎盘超声图像自动分级这一临床应用问题,提出一种基于自适应多神经网络的分级算法。该算法与一般的一次性分离算法不同,其是通过设计两级BP神经网络模型来对胎盘图像进行两级分离。该算法在神经网络的训练中,对神经网络的输出没有采用一般的四舍五人来得到胎盘级数,而是采用了更合理的胎盘级数判定准则,并由此提出了一种自适应确定阈值的算法,用来判定胎盘级数。实验及临床应用表明:该算法能得到与专家手工分级基本吻合的自动分级结果,其阈值分割前得出的分级结果更可以给医生一个精确的定量衡量胎盘成熟期的参考,因此具有较好的临床应用前景。

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