8 January 2008 Classification of FTIR cancer data using wavelets and BPNN
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In this paper, a feature extracting method based on wavelets for horizontal attenuated total reflectance Fourier transform infrared spectroscopy (HATR-FTIR) cancer data analysis and classification using artificial neural network trained with back-propagation algorithm is presented. 168 Spectra were collected from 84 pairs of fresh normal and abnormal lung tissue's samples. After preprocessing, 12 features were extracted with continuous wavelet analysis. Based on BPNN classification, all spectra were classified into two categories : normal or abnormal. The accuracy of identifying normal, early carcinoma, and advanced carcinoma were 100%, 90% 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 lung cancer in its early stages.
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Cungui Cheng, Yumei Tian, Changjiang Zhang, "Classification of FTIR cancer data using wavelets and BPNN", Proc. SPIE 6826, Optics in Health Care and Biomedical Optics III, 682633 (8 January 2008); doi: 10.1117/12.758497; https://doi.org/10.1117/12.758497

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