用人工神经网络解释尿流图

S. Altunay, Z. Telatar, O. Eroğul, E. Aydur
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引用次数: 4

摘要

尿流法是一种测量方法,通过测量和绘制空尿量变化率,提供患者下尿路动力学的数值和图形信息。该研究的主要目的是利用人工神经网络(ANN)评估尿流量数据,并为泌尿科专家提供预诊断结果。神经网络采用反向传播方法进行训练,神经网络的输入是一种特殊的特征提取算法的结果,该算法是根据泌尿外科专家的建议设计的。系统的成功是通过一组数据来监测的,这些数据已经被专家诊断出来。人工神经网络的输出分为“健康”、“可能病理”和“病理”三组。
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Interpretation of Uroflow Graphs with Artificial Neural Networks
Uroflowmetry is a measuring method, which provides numerical and graphical information about patient's lower urinary tract dynamics by measuring and plotting the rate of change of voided urine volume. The main purpose of the study is to evaluate uroflowmetric data using artificial neural networks (ANN) and provide a pre-diagnostic result for urology specialists. The ANN is trained using back-propagation method and the inputs of ANN are the results of a special feature extraction algorithm, which is designed with the suggestions of urology specialists. System's success is monitored with a set of data, which was already diagnosed by specialists. The outputs of ANN are classified into three groups, namely, "healthy", "possible pathologic" and "pathologic"
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