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Diagnostic Analysis of liver B Ultrasonic Texture Features Based on LM Neural Network

机译:基于LM神经网络的肝B超声纹理特征诊断分析

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In this study, B ultrasound images of 124 benign and malignant patients were randomly selected as the study objects. The B ultrasound images of the liver were treated by enhanced de-noising. By constructing the gray level co-occurrence matrix which reflects the information of each angle, Principal Component Analysis of 22 texture features were extracted and combined with LM neural network for diagnosis and classification. Experimental results show that this method is a rapid and effective diagnostic method for liver imaging, which provides a quantitative basis for clinical diagnosis of liver diseases.
机译:在这项研究中,将B 24良性和恶性患者的B超声图像作为研究对象随机选择。通过增强的去噪处理肝的B超声图像。通过构造反映每个角度信息的灰度共发生矩阵,提取了22个纹理特征的主要成分分析,并与LM神经网络结合进行诊断和分类。实验结果表明,该方法是肝脏成像的快速有效的诊断方法,为肝脏疾病的临床诊断提供了定量依据。

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