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Combined with terahertz spectroscopy, partial least squares-discriminant analysis and support vector machines could be novel and effective diagnosis methods for cervical cancer.

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Combined with terahertz time-domain spectroscopy, the feasibility of fast and reliable diagnosis of cervical carcinoma by a fuzzy rule-building expert system (FuRES) and a fuzzy optimal associative memory (FOAM) had been studied. The terahertz spectra of 52 specimens of cervix were collected in the work. The original data of samples were preprocessed by Savitzky-Golay first derivative (χderivative), principal component orthogonal signal correction (PC-OSC) and emphatic orthogonal signal correction to improve the performance of FuRES and FOAM models. The effect of the different pretreating methods to improve prediction accuracy was evaluated. The FuRES and FOAM models were validated using bootstrapped Latin-partition method. The obtained results showed that the FuRES and FOAM model optimized with the combination S-G first derivative and PC-OSC method had the better predictive ability with classification rates of 92.9 ± 0.4 and 92.5 ± 0.4 %, respectively. The proposed procedure proved that terahertz spectroscopy combined with fuzzy classifiers could supply a technology which has potential for diagnosis of cancerous tissue.