Comparison of the Performance of Linear Multivariate Analysis Methods for Normal and Dyplasia Tissues Differentiation Using Autofluorescence Spectroscopy
| Autor: | Shou Chia Chu, Chih-Yu Wang, J.K. Lin, Huihua Kenny Chiang, Tzu-Chien Ryan Hsiao |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Multivariate statistics
Epithelial dysplasia Pathology medicine.medical_specialty Multivariate analysis business.industry Biomedical Engineering Reproducibility of Results Sensitivity and Specificity Autofluorescence Spectrometry Fluorescence Tubular adenoma Artificial Intelligence Neoplasms Bayesian multivariate linear regression Multivariate Analysis Principal component analysis Partial least squares regression Linear Models Humans Medicine Diagnosis Computer-Assisted business Algorithms |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 53:2265-2273 |
| ISSN: | 1558-2531 0018-9294 |
| DOI: | 10.1109/tbme.2006.883643 |
| Popis: | We compared the performance of three widely used linear multivariate methods for autofluorescence spectroscopic tissues differentiation. Principal component analysis (PCA), partial least squares (PLS), and multivariate linear regression (MVLR) were compared for differentiating at normal, tubular adenoma/epithelial dysplasia and cancer in colorectal and oral tissues. The methods' performances were evaluated by cross-validation analysis. The group-averaged predictive diagnostic accuracies were 85% (PCA), 90% (PLS), and 89% (MVLR) for colorectal tissues; 89% (PCA), 90% (PLS), and 90% (MVLR) for oral tissues. This study found that both PLS and MVLR achieved higher diagnostic results than did PCA. |
| Databáze: | OpenAIRE |
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