摘要
为了快速无创客观地检测舌体所携带的病理信息,提出了基于概率神经网络舌诊归一化反射率近红外光谱分类诊断模型。采集了健康人、脂肪肝患者、肝炎患者三类受试者舌尖的光谱数据,进行归一化反射率预处理,各获得32个样本数据,随机从三类受试者各抽取24个舌尖的光谱数据用于建模,其余的24个用于预测,并分析了径向基函数的扩展速度（SPREAD）对网络的影响。得出SPREAD在0.0065~0.0077之间取值时对网络模型的预测效果和泛化能力较好,用该模型对24个预测样本进行预测,其正确率为95.8%。实验结果表明利用概率神经网络对舌诊近红外光谱的分类是可行的,说明舌表面的光谱信息能客观的反映人体的病理信息,利用此方法对疾病的快速无创地进行初步的筛查成为可能。
For a quick,objective and non-invasive examination of the tongue for pathological information,a near infrared spectral classification examination model,based on probability neural network tongue diagnosis after normalized reflectivity pretreatment,is proposed.With the collection of spectral data from the tip of the tongues from healthy people,fatty liver patients,and hepatitis patients,32 sample data from each group are collected after normalization reflectivity pretreatment.24 sample data from each case are selected for the construction of the models.Then the remaining 24 sample data are adopted for prediction.Also the influence of the spread of radial primary function upon the internet is analyzed.The conclusion is that the predictability and generalization of the network models work well when the spread ranges from 0.0065 to 0.0077.The predictions,based on the model,toward the samples are 95.8% correct.Experimental results show that the near infrared spectral classification of the tongue diagnoses in accordance with probabilistic neural network is feasible,and it is proved that spectral information of the tongue surface can objectively reflect human pathological information,so the preliminary screening which is rapid and noninvasive is probable with the help of this method.
出处
《激光与红外》
CAS
CSCD
北大核心
2010年第11期1201-1204,共4页
Laser & Infrared
基金
国家自然科学基金(No.30973964
No.60674111)资助
关键词
近红外光谱
舌诊
概率神经网络
near-infrared spectroscopy
tongue diagnosis
probabilistic neural network
