کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2109168 | 1083862 | 2012 | 7 صفحه PDF | دانلود رایگان |

Objective: The feasibility of early diagnosis of endometrial carcinoma was studied by least squares support vector machines (LS-SVM) and fuzzy rule-building expert system (FuRES) that classified near infrared (NIR) spectra of tissues. Methods: NIR spectra of 77 specimens of endometrium were collected. The spectra were pretreated by principal component orthogonal signal correction (PC-OSC) and direct orthogonal signal correction (DOSC) methods to improve the signal-to-noise ratio (SNR) and remove the influences of background and baseline. The effects of modeling parameters were investigated using bootstrapped Latin-partition methods. Results: The optimal LS-SVM model of the PC-OSC pretreatment method successfully classified the samples with prediction accuracies of 96.8 ± 1.4%. Conclusions: The proposed procedure proved to be rapid and convenient, which is suitable to be developed as a non-invasive diagnosis method for cancer tissue.
Journal: Cancer Epidemiology - Volume 36, Issue 3, June 2012, Pages 317–323