Classification of Rat FTIR Colon Cancer Data Using Wavelets and BPNN

本文提出了一种新的基于水平衰减全反射-傅里叶变换红外光谱（HATR-FTIR）的小波特征提取与反向传播人工神经网络模式分类方法以提高FTIR对早期大鼠结肠癌的诊断准确率．对60只DMH诱导的SD大鼠，44只诱导鼠的第二代鼠，36只正常SD大鼠的结肠正常组织、异常增生、早癌及进展期癌组织所获得的的HATR-FTIR，利用连续小波多尺度分析法提取12个特征量，采用反向传播人工神经网络进行分类，识别准确率分别为100%、94%、97.5%及100%．实验结果表明此方法对早期结肠癌具有较高的诊断率．

Classification of Rat FTIR Colon Cancer Data Using Waveletsand BPNN

A feature extracting method based on wavelets for horizontal attenuated total reflectance Fourier transform infrared spectroscopy (HATR‐FTIR) and the cancer classification using artificial neural network trained with back‐propagation algorithm is presented. The FTIR data collected from 36 normal Sprague‐dawley (SD) rats, 60 1,2‐DMH‐induced SD rats, and 44 second generation rats of those induced rats were first preprocessed. Then, 12 feature variants were extracted using continuous wavelet analysis. Based on BPNN classification, all spectra were classified into two categories: normal and abnormal ones. The accuracy values of identifying normal, dysplastic, early carcinoma, and advanced carcinoma were 100%, 94%, 97.5%, and 100%, respectively. This result indicated that FTIR with continuous wavelet transform (CWT) and the back‐propagation neural network (BPNN) could effectively and easily diagnose colon cancer in its early stages.