CAJ | 학술논문

临床上对早期脂肪肝的准确诊断具有较大难度,文中采用Wistar大鼠动物实验数据,研究早期脂肪肝的识别方法。通过提取肝脏超声射频信号的多个特征参数及假设检验得到的最佳特征矢量,再利用BP神经网络结合模糊函数对脂肪肝程度进行量化。结果表明,正常肝和轻度脂肪肝的识别率分别为97.50和94.29,假阴性率和假阳性率分别为2.5和5.71。
림상상대조기지방간적준학진단구유교대난도,문중채용Wistar대서동물실험수거,연구조기지방간적식별방법。통과제취간장초성사빈신호적다개특정삼수급가설검험득도적최가특정시량,재이용BP신경망락결합모호함수대지방간정도진행양화。결과표명,정상간화경도지방간적식별솔분별위97.50화94.29,가음성솔화가양성솔분별위2.5화5.71。
It' s difficult to diagnose mild fatty liver correctly in clinic,so the research of recognition of mild fatty liver by using animal experiment data of Wistar rat is proposed in this paper.Firstly several characteristic parameters are selected from ultrasound radiofrequency signal of livers,then hypothesis testing is used to obtain the best characteristic vector,finally BP neural network combined with Fuzzy function is used to quantify the degree of fatty livers.The results show that,the accuracy rates of classification is 97.50 and 94.29 for normal liver and mild fatty liver separately,and false negative rate and false positive rate is 2.5 and 5.71 separately.